Normalized Difference Moisture Index

The number of vegetation indices supported by GeoPard is continuously growing. GeoPard team introduces the Normalized Difference Moisture Index (NDMI). The index determines vegetation water content and normalized difference water index (NDWI). It is useful for finding the spots with existing water stress in plants.

Lower NDMI values mark the spots where the plants are under stress from insufficient moisture.
On the other side, lower normalized difference water index values following the vegetation peak highlight the spots that are becoming ready for harvesting first.

The difference of the vegetation relative water content between two satellite images (Sentinel-2 constellation in this case)

The difference of the vegetation relative water content between two satellite images (Sentinel-2 constellation in this case)

In the following screenshots, you can find the NDMI zones generated based on June 19 (vegetation peak) and July 6 satellite images and the equation map representing the NDMI difference.

Normalized Difference Moisture Index calculated on top of Planet / Sentinel-2 / Landsat imageNDMI calculated on top of Planet / Sentinel-2 / Landsat image

What is Moisture Index?

It is a measure or calculation used to assess the moisture content or availability in a specific area or region. It is typically derived from various environmental factors such as precipitation, evapotranspiration, soil properties, and vegetation cover.

It provides a relative indication of the wetness or dryness of an area, helping to identify potential water stress or drought conditions.

It is a valuable tool for monitoring and managing water resources, agricultural planning, and understanding the ecological conditions of a particular region.

What is Normalized Difference Moisture Index?

Normalized Difference Moisture Index (NDMI) is a vegetation index derived from remote sensing data to assess and monitor the moisture content of vegetation. Like other vegetation indices, it is computed using spectral reflectance values from satellite or aerial imagery.

It is particularly useful in monitoring plant water stress, assessing drought conditions, estimating fire risk, and studying the impacts of climate change on vegetation.

It is calculated using the Near-Infrared (NIR) and Shortwave Infrared (SWIR) bands, which are sensitive to the moisture content in vegetation. The formula for NDMI is:

NDMI = (NIR – SWIR) / (NIR + SWIR)

NDWI values typically range from -1 to 1, with higher values indicating higher vegetation moisture content and lower values indicating lower moisture content or water stress in the vegetation. Negative NDMI values can be associated with non-vegetated areas or areas with very low moisture content.

What is NDWI?

NDWI, or Normalized Difference Water Index, is a remote sensing index used to quantify and assess water content or water-related features in vegetation or landscapes.

It is calculated by analyzing the reflectance of near-infrared and green light bands from satellite or aerial imagery. It is particularly useful for identifying water bodies, monitoring changes in water availability, and assessing vegetation health.

By comparing the absorption and reflection of different wavelengths, it provides valuable information for applications such as drought monitoring, hydrological analysis, and ecosystem management.

Visualization of NDMI to Determine Normalized Difference Water Index

Visualizing the NDMI involves processing satellite or aerial imagery, calculating the NDMI values, and then displaying the results as a color-coded map or image. Here are the general steps to visualize NDMI:

  • Acquire satellite or aerial imagery: Obtain multispectral imagery from a satellite or aerial platform, such as Landsat, Sentinel, or MODIS. Ensure that the imagery includes the necessary bands: Near-Infrared (NIR) and Shortwave Infrared (SWIR).
  • Pre-process the imagery: Depending on the data source, you may need to preprocess the imagery to correct for atmospheric, geometric, and radiometric distortions. Convert the digital numbers (DN) in the image to spectral reflectance values.
  • Calculate NDMI: For each pixel in the image, use the NIR and SWIR reflectance values to compute the NDMI using the formula: NDMI = (NIR – SWIR) / (NIR + SWIR).
  • Color mapping: Assign a color palette to the NDMI values. Typically, a continuous color scale is used, ranging from one color (e.g., red) for low NDMI values (indicating low moisture content) to another color (e.g., green) for high NDMI values (indicating high moisture content). You can use software like QGIS, ArcGIS, or programming libraries like Python’s Rasterio and Matplotlib to create a color map.
  • Visualize the NDMI map: Display the NDMI map or image using GIS software, a programming library, or an online platform. This will allow you to analyze the spatial distribution of vegetation moisture content and identify areas of water stress or high moisture.
  • Interpretation and analysis: Use the NDWI visualization to assess vegetation health, monitor drought conditions, or evaluate fire risk. You can also compare normalized difference water index maps from different time periods to analyze changes in vegetation moisture content over time.

Remember that different software tools or programming libraries may have slightly different workflows, but the overall process will be similar. Additionally, you can overlay other data layers, such as land use, elevation, or administrative boundaries, to enhance your analysis and better understand the relationships between vegetation moisture content and other factors. 

Automated Crop Scouting with Intersection of Data Layers

In GeoPard we have a module to create crop data scouting zones Automatically using flexible configuration of business and agronomic logic.

It allows to control huge amount of fields and do scouting only when emergency case happened.

Business/agronomic logic could be flexible. In this example – Tasks are created in the areas where we have High Historical Field Potential Zones and Low vegetation on the latest satellite imagery.

An example of another use-case: Low Yield zone (from yield file) Intersected with Low pH zones – to adjust lime fertility levels.

 

Automated Crop data Scouting zones with the Intersection of Data Layers
High Historical Field Productivity zones intersected with the latest Planet image low vegetation Zones -> Scouting tasks are created automatically in GeoPard

For crop trading companies and data modelers intersection between Historically most stable and High yield zones could be a good indicator to extrapolate Yield predictions.

If you’re a farmer, agronomist, or precision agriculture specialist, you know the importance of crop data scouting. It’s essential for monitoring the health of your crops and identifying any potential issues before they become major problems.

However, traditional crop scouting can be time-consuming and labor-intensive. That’s where automated scouting tasks come in.

GeoPard is a revolutionary automated precision agriculture software that uses advanced algorithms and satellite imagery to automatically monitor your crops. With GeoPard, you can easily set up automated scouting tasks that will alert you to any potential issues, such as pests, diseases, or nutrient deficiencies.

One of the key benefits of using automated scouting tasks is the ability to quickly and accurately identify issues in your crops. GeoPard uses advanced algorithms to analyze the satellite imagery of your fields, detecting even the smallest changes in your crops.

This means you can quickly identify any potential problems and take action to address them before they become more serious.

Another advantage of automated scouting tasks is the ability to monitor your crops on a regular basis. With traditional scouting, it can be difficult to regularly visit your fields and check for potential issues.

But with GeoPard, you can set up automated tasks that will monitor your crops on a daily or weekly basis, giving you a more comprehensive view of their health.

GeoPard’s automated scouting tasks are also customizable, allowing you to tailor them to your specific needs. You can set up tasks to monitor for specific issues, such as pests or diseases, or set up tasks to monitor specific areas of your field. This means you can get the information you need to make informed decisions about your crops.

In addition to its automated scouting tasks, GeoPard also offers a range of other features that can help you manage your precision agriculture operations. You can use GeoPard to plan your planting and fertilization, monitor soil moisture levels, and track your yield.

Overall, GeoPard’s automated scouting tasks are a powerful tool for farmers, agronomists, and precision agriculture specialists. With GeoPard, you can quickly and easily monitor your crops and identify potential issues, helping you make better decisions about your operations.

What is Crop Scouting?

Crop scouting is a practice in agriculture that involves systematically inspecting and monitoring crops to assess their health, growth, and potential issues. It typically involves physically walking through fields or utilizing technology such as drones or sensors to gather data.

Crop scouts observe and collect information on factors like pest infestations, disease outbreaks, nutrient deficiencies, and weed pressure.

This data helps farmers make informed decisions regarding crop management, such as implementing targeted treatments, adjusting fertilizer applications, or implementing pest control strategies. It plays a crucial role in maximizing crop yields and ensuring overall crop health.

What is Automated Crop Data Scouting?

Automated crop scouting refers to the application of cutting-edge technologies, including robotics, unmanned aerial vehicles (UAVs), various sensors, and artificial intelligence (AI), to observe and evaluate crop health and development in an agricultural environment.

The goal is to enhance effectiveness, lower expenses, and streamline crop management by automating tasks traditionally performed by human crop scouts.

The process of automated crop data scouting entails several stages, such as:

  • Gathering data: UAVs or terrestrial robots fitted with a range of sensors (e.g., cameras, multispectral sensors, LIDAR) acquire information on crop conditions, encompassing plant health, pest and disease occurrence, soil properties, and nutrient concentrations.
  • Analyzing data: The gathered data is subsequently processed and examined using AI and machine learning algorithms to detect patterns, irregularities, and tendencies related to crop health and development.
  • Making decisions: The data analysis results can be utilized to make informed choices about crop management, including optimizing watering, fertilization, pest management, and other interventions.
  • Taking action: Farmers can implement targeted measures based on the knowledge acquired from automated crop monitoring to address specific problems in the field, such as applying pesticides or nutrients solely where required, minimizing waste and environmental impact.

By providing farmers with real-time, accurate data, it can significantly enhance agricultural productivity and sustainability, allowing for better decision-making and the implementation of more precise management techniques.

How to Identify Scouting Zone?

Determining crop data scouting zones involve dividing a farm field into smaller, manageable sections based on aspects such as soil composition, terrain, historical crop outcomes, or other pertinent factors.

The objective is to establish uniform areas representing similar conditions, enabling more focused scouting, observation, and management practices. Here’s a step-by-step method to pinpoint crop scouting zone:

  • Collect historical information: Compile data on previous crop yields, soil analysis results, occurrences of pests and diseases, and any other significant information for the field. This data can help recognize areas with comparable conditions or performance.
  • Examine soil composition and terrain: Investigate the soil types and terrain of your field to comprehend natural variations. Different soil compositions and elevation levels can influence crop growth, nutrient absorption, and water accessibility, which in turn affects crop health.
  • Utilize remote sensing technology: Use satellite or drone-based imagery to obtain additional details on field conditions, such as vegetation indices, soil moisture levels, and temperature variations. This information can help fine-tune scouting zones by providing a more comprehensive view of the field.
  • Implement precision agriculture techniques: Use precision agriculture software to process and analyze the gathered data. These tools can help identify patterns and establish data-driven scouting areas, considering factors like crop health, soil variability, and terrain.
  • Establish scouting areas: Based on the data analysis, segment the field into smaller, uniform areas that display similar traits. These areas should be manageable in size and adapted to the specific requirements of your operation.
  • Update and adjust regularly: As circumstances change and new data becomes available, reassess and modify the scouting areas to ensure they remain relevant and precise. This may involve updating the areas based on new yield data, occurrences of pests and diseases, or other factors that influence crop performance.

Hence, by pinpointing and creating crop scouting zone, farmers can concentrate their monitoring efforts more efficiently and apply targeted management practices, resulting in better resource usage and improved crop health.

Normalized Difference Vegetation Index (NDVI) Make Farmer Life Easier

Normalized Difference Vegetation Index (NDVI) is a commonly used metric to quantify the density and health of vegetation. Its values range from -1 to 1, with negative values indicating water or bare soil, values near zero indicating sparse vegetation, and higher values indicating denser and healthier vegetation.

What is Normalized Difference Vegetation Index (NDVI)?

It is a method that calculates the variation between the quantity of red light received by vegetation and the quantity of near-infrared light that is strenuously reflected by vegetation.

The objective of this method is to provide a quantitative analysis of the state of plant life. There is no situation in which the its value falls outside of the spectrum of -1 to +1. However, there isn’t a clear demarcation between the many types of land cover that may be found.

If the sum of the figures comes out to be less than zero, it is quite probable that the substance in question is water. If you obtain an NDVI score that’s quite near to a positive one, there’s a good chance that it’s just a bunch of tightly packed green leaves. This is especially true if the leaves are densely packed together.

Green leaves have a greater value than red leaves do, which is why this is the case. Imagine for a moment that the it is very close to being equal to 0.

In such a situation, there is hardly a snowball’s chance in hell that any leaves of any type are still there, and the region may even be urbanized by this point. The Normalized Difference Vegetation Indicator is the index that is used by analysts in the area of remote sensing the majority of the time.

Why is Normalized Difference Vegetation Index useful?

There are a lot of different vegetation indices, and the vast majority are comparable to one another. However, it is the one that is used the most often and widely, and it also has an essential benefit, which is a high resolution of pictures that are derived from satellite data.

In the circumstances like this, channels with a resolution of ten meters may be utilized to determine the NDVI. Remember that one pixel is equal to ten by ten meters. On the other hand, the index’s resolution that uses extra light channels, namely red age, might be twenty meters, where one pixel is equal to twenty by twenty meters.

How is the NDVI calculated?

It may be determined using the following straightforward mathematical procedure, which converts raw satellite information into vegetation indices.

Normalized Difference Vegetation Index formula

The equation creates a single number that is representative and integrates the information that is accessible in the red and NIR (near-infrared) bands.

To do this, it takes the reflectance throughout the red spectral band and subtracts it from the reflectance throughout the NIR band. After that, the result is divided by the total reflectance of the NIR and red wavelengths.

The assessment of the NDVI will never be more than a positive one and less than a negative one. In addition, a number between -1 and 0 denotes a plant that has died and inorganic items like stones, roads, and buildings.

Simultaneously, its values for living plants may vary anywhere from 0 to 1, with 1 representing the healthiest plant and 0 representing the unhealthiest plant. It is possible to assign a single value to each pixel in a picture, whether that pixel represents a single leaf or a wheat field that spans 500 acres.

How do we use Normalized Difference Vegetation Index?

Justifiably, it is now being utilized in a number of different fields of research. For instance, it is leveraged in the field of agriculture for the objectives of precision farming and the evaluation of biomass. It is likewise employed by foresters in order to evaluate forest resources as well as the leaf area index (LAI).

In addition, NASA believes it to be a reliable indication of the existence of drought conditions. The proportional NDVI and the concentration of vegetation are both lower in areas where water serves as a barrier to the establishment of vegetation.

This is because water prevents the roots of plants from growing deeper into the soil. It, including other kinds of remote sensing, has the capability to be utilized in a wide variety of distinct ways in reality.

What can NDVI tell us about plants?

It is essential to have a solid comprehension that the Normalized Difference Vegetation Index is only an indication of the healthiness of the plant and provides no information about the reasons behind a certain condition.

The vegetation index is more of an expression than a direct reflection of what is occurring on the field. Let’s look at three applications of NDVI for field analysis:

When a new season begins

It is beneficial for understanding the plant’s winter hardiness and how it managed to survive.

  • If its value is less than 0.15, it is somewhat likely that all of the plants in this field section have perished. Typically, these numbers relate to the tilled soil without any plants.
  • Another example of a low number is 0.15-0.2. It might suggest that the plants began preparing for winter during the early phenological period, before the tilling stage.
  • A result in the range of 0.2 to 0.3 is satisfactory. The plants most likely progressed to the tilling stage and have regained their vegetative state.0.3−
  • 0.5 is a decent value. However, it is important to remember that higher NDVI readings suggest that plants overwintered at a later phenological stage. Suppose the satellite picture was captured before the vegetation resumed its normal state. In that case, analyzing the area after the vegetation continued its normal form is important.
  • A number greater than 0.5 indicates an anomaly during the post-wintering phase. It is recommended that you check out this field zone.

To recap, if you notice that the Values obtained are significantly different from the norm, you need to conduct an inspection of the relevant portion of the field. A large departure from the norm is required for values to be categorized as abnormal in a given area.

When the season is in the middle

Utilizing the index might be helpful in gaining a better understanding of how plants develop. Imagine that the readings fall between mild and high (0.5-0.85). It is highly likely that this particular part of the area does not face any major challenges at the present time.

If the index remains lower than it should be, there may be issues such as a deficiency of soil water or nutrients. However, you need to do your own investigation into this particular area.

We generate maps for variable-rate application (VRA) of nitrogen by using the Normalized Difference Vegetation Index. We identify regions with vegetation indices ranging from low to high.

After that, it is up to the individual farmer to determine the amount of necessary fertilizer. The following is the most effective method for applying nitrogen:

  • Suppose the vegetation index for the region is high. In that case, the recommended dosage of fertilizer should be decreased to 10 and 30 percent of the typical rate.
  • If the vegetation index is around average, the recommended dosage of fertilizer should be raised to between 20 and 25 percent of the typical amount.
  • If the vegetation index is low, you first need to figure out why it is that way.

To reconstruct a field’s agricultural yield, we also use this index. With this data, we produce maps that may be used for the variable-rate application of potassium and phosphate fertilizers.

When the season is over

The NDVI index is a useful tool for determining whether fields are ready to be harvested; the lower the index, the more closely a portion of the area is approaching the stage when it is ready to be harvested. In this scenario, a value for the index lower than 0.25 would be ideal.

NDVI index is a useful tool for determining whether fields are ready to be harvested

To begin, it is a mathematical computation performed pixel-by-pixel on an image utilizing tools from a GIS (Geographic Information System). Calculated by contrasting the amounts of red and near-infrared light absorbed and reflected by the plant, it measures the plant’s overall state of health.

The Normalized Difference Vegetation Index may be used to study land all over the globe, making it ideal for focused field studies and national or global vegetation monitoring.

By means of utilizing NDVI, we can get an immediate analysis of fields, enabling agriculturalists to optimize the production potential of areas, limit their influence on the environment, and modify their precision agricultural operations.

Moreover, examining it in conjunction with other data streams, such as those about the weather, might provide further insight into recurring patterns of droughts, freezes, or floods and how they impact vegetation.


Frequently Asked Questions


1. What is NDVI primarily used to determine?

It is primarily used to determine the health and density of vegetation in a given area. This index is widely used in agriculture, forestry, and ecology to monitor vegetation growth, assess plant stress levels, identify areas of drought or disease, and aid in crop management decisions.

2. How to read NDVI imagery?

To read NDVI imagery, you can interpret the color scale associated with the index values. Typically, healthy vegetation appears green, while less healthy or sparse vegetation appears yellow or red.

Darker shades may indicate areas with high biomass, while lighter shades may suggest lower vegetation density or the presence of bare soil.

Understanding the context of the area being analyzed, such as the specific crop type or environmental conditions, can further assist in interpreting NDVI imagery and making informed decisions about agricultural practices.

Farm/Crop Yield Data Monitoring and Calculation in Agriculture

In agriculture, yield mapping is a method that uses GPS data to assess factors, including farm/crop yield and moisture levels in a particular field. It may also be referred to as yield monitoring.

It was created in the 1990s and used a mix of GPS and tangible sensors such as speedometers to monitor farm yields, grain elevator performance, and combine speed all at the same time.

Meanwhile, monitors of yield are a vital component of many different site-specific management strategies. Yield maps, also known as yield monitors’ visual and analytical outcomes, inspire innovative research and may offer trustworthy answers to properly executed on-farm experiments.

Yield monitors (also known as yield gages) measure the amount of crop produced. The feedback provided by yield maps allows for determining the impacts of controlled inputs like fertilizer & lime, seed & pesticides, and artistic techniques like tillage, irrigation, and drainage.

When utilized in conjunction with a combine that is also fitted with a differentially-corrected global positioning system (DGPS) receiver, a yield monitor is at its most effective.

The yield monitor data system concurrently records yield, grain moisture, and position data. These are the fundamental crop yield data that are required to make yield maps.

A yield map will include a variety of colors and shades, and each one will reflect a diverse range of productivity or crop production. Yield maps help gain a more excellent knowledge of the magnitude and position of yield variability within a field.

Investigating the qualities of the soil and the field’s other aspects should be done since there are patterns of variability. “Yield maps validate the recollections that you should have had” is a phrase that has been repeated several times.

What is Yield in Agriculture?

The quantity of seeds or grains that may be harvested from a particular land area is referred to as the yield. The most common units of measurement for it are kilos per hectare or bushels per acre.

Using an indicator such as the average farm yield per acre helps examine a farmer’s agricultural production on a specific field over a certain length of time.

Because it represents the outcome of all of the labor and resources put by agrarians in the growth of plants in their fields, it is regarded as perhaps the most essential gauge of each farmer’s competence.

A permanent and visible record of the harvested yields may be provided through yield maps. On the other hand, the variability in yield from a single year does not give sufficient information to identify long-term patterns in productivity.

During the analysis process, it is necessary to consider variables such as the fertility of the soil, the amount of rainfall, and the weed pressure.

Ensure you save the raw crop yield data used to create the maps in at least two different secure locations.

Although you have previously created a map, you may need the original data again while either implementing new management and decision-making software or updating computer systems.

As more years of data become accessible, there will be more confidence in comprehending the factors that produce variability, and the value of historical data will skyrocket.

The examination of long-term production records may help evaluate the productivity and viability of soil and the suitability of the cultural methods employed to cultivate a crop.

Even while variations in soil types or soil qualities are often the cause of yield variance within a field, weather patterns typically significantly influence variability.

The first three to five years of yield data collection should be deemed to have limited significance since not enough information will have been gathered to account for the variability in yield caused by weather.

How Is Farm/Crop Yield Calculated In Agriculture?

Typically, farmers would count how much of a specific crop has been harvested from a particular area before estimating the crop’s yield. After that, the crop that has been gathered is given a weight, and the crop yield of the whole farm is projected from that sample.

Suppose a wheat farmer recorded 30 heads per foot squared, and each head included 24 seeds. Now, if they assumed that 1,000 kernels weighed 35 grams, then the yield approximated using the simple method would be 30 times 24 times 35 times 0.04356, which equals 1,097 kilograms per acre.

Again, remember that this estimate is based on the assumption that the weight of 1,000 kernels is 35 grams. In addition, since one bushel of wheat weighs 27.215 kilograms, we calculated that the expected yield would be 40 bushels per acre (1097 divided by 27.215).

The term “crop yield” may also refer to the number of seeds produced by the plant. For instance, if one grain of wheat resulted in three other grains of wheat, the yield would be 1:3. “Agricultural production” is also sometimes used interchangeably with “farm/crop yield.”

Note: In a global economy, this data is essential to determine whether or not the crops that are grown will sufficiently offer food for a state’s food supply, animal feed, and energy sources.

Farm/Crop Yield Data Features

Here we discuss some of the significant farm yield data features.

The More Comprehensive Analyses

To carry out multi-layer analysis, you must first compile numerous layers of data into a single map and then search for connections between the various data layers.

It should be possible to produce combined productivity zones by using vegetation indices derived from satellite images, topography, and data from equipment, including yield, electrical properties, moisture levels, and others, as well as the findings of agrochemical analysis and 3D maps.

Automatic Visualization

To provide a better comprehension of the field’s variability and the development of management zones, the raw crop yield data should have been transformed into a gradient uniformly distributed picture.

Each of the yield file characteristics may be seen in graphical form, including moisture, yield mass, yield volume (wet and dry), downforce, fuel consumption, etc.

How Is Farm and Crop Yield Calculated In Agriculture?

Correction of Raw Data

A unique point in the field may get smoothed out (for instance, working over a portion of the combined header that is less than its whole width). You should be able to adjust isolated zones and polygons while producing farm yield data based on zones.

Construction of Prescription Maps

Prescription maps give input rates for specific zones of a field. These maps are derived using various spatial data, like soil nutrient concentrations and historical yields.

Closing Remarks

It is only possible to illustrate yield variability via yield maps. Their accuracy is only as good as the data used to create them. To collect reliable data, monitors need to have their settings properly configured and be reviewed often.

To understand the factors that contribute to variability, the crop yield data from maps, along with those from soil tests, scouting notes, and other observations, should be utilized.

Farmers are equipped with the information necessary to make better management choices, which have a good impact on the environment and result in increased production and profitability. This knowledge may get achieved via site-specific crop management.

Remote crop monitoring system: How does it work?

A remote crop monitoring system in precision agriculture refers to the use of various technologies and tools to monitor and manage crops from a distance. This approach leverages data collection, analysis, and communication technologies to make informed decisions about crop health, irrigation, fertilization, and overall farm management.

Farming is not left behind in the 21st century where everything is going digital. As you read this article, several farmers spread across the globe are already using tech to perform several tasks in their fields such as monitoring plant humidity, soil conditions, general health, temperatures, and even many more the use of sensors.

By embracing technology, farmers are enjoying the benefits of having accurate statistics as compared to the old days when they used guesses works and intuitions to make choices. This helps them in making better judgments that result in increased harvests.

What is crop monitoring system?

Crop monitoring refers to the process of systematically observing, assessing, and collecting data about crops throughout their growth cycle.

It involves regular and systematic observation of crops to gather information about their health, growth, and development.

Its goal is to make informed decisions regarding crop management practices, optimize resource utilization, and maximize yields. It typically involves the following activities:

  • Visual Inspection
  • Phenological Observations
  • Soil Monitoring
  • Weather Monitoring
  • Sensor-Based Monitoring
  • Data Analysis

By monitoring crops, farmers can proactively address problems, optimize resource allocation, and make informed decisions to improve overall crop health, yield, and profitability. It is an essential component of precision agriculture, enabling farmers to practice targeted and sustainable crop management.

Smart crop monitoring system: How does it work?

Its main goal is to ensure that farming becomes easier and more profitable as compared to traditional methods. From displaying data about the fields all the way to weather forecasts, below is all that you may want to know about remote crop monitoring and related topics.

A smart crop monitoring system incorporates various technologies to collect, analyze, and utilize data for efficient crop management. Here’s a breakdown of how it typically works:

1. Sensor Deployment

The system begins by deploying sensors in the field. These sensors can measure parameters such as soil moisture, temperature, humidity, nutrient levels, and light intensity.

They may also include weather sensors to capture data on rainfall, wind speed, and solar radiation. The sensors are strategically placed throughout the field to gather representative data.

2. Data Collection

The deployed sensors continuously collect data from the field. This can be done using wired or wireless connections.

Wireless sensors are commonly used as they provide flexibility and ease of deployment. The collected data is sent to a central system for further processing and analysis.

3. Data Transmission

Wireless sensors transmit the collected data to a central hub or gateway. This can be done using various wireless communication technologies such as cellular networks, Wi-Fi, or dedicated radio systems. The data transmission can be in real-time or at regular intervals depending on the system’s configuration.

4. Data Storage and Processing

The collected data is stored in a database for further analysis. Advanced data processing techniques, including machine learning algorithms and statistical models, are applied to the data to extract meaningful insights and patterns. This analysis helps identify correlations, trends, and anomalies in the crop conditions.

5. Decision Support and Alerts

Based on the analyzed data, the system provides decision support to farmers or agronomists. It generates alerts and notifications regarding critical events, such as soil moisture levels dropping below a threshold or the presence of pests or diseases.

These alerts are delivered through web-based dashboards, mobile applications, or email/SMS notifications, enabling timely interventions.

6. Visualization and Reporting

The system presents the analyzed data in a user-friendly manner through visualizations and reports. Graphs, charts, and maps are often used to convey information about crop health, growth patterns, and environmental conditions. This helps farmers interpret the data easily and make informed decisions.

7. Automation and Control

In some cases, it can be integrated with automated irrigation systems, fertigation systems, or machinery.

Based on the collected data and analysis, the system can automatically control irrigation schedules, adjust nutrient application rates, or activate pest management measures.

This integration allows for real-time, data-driven decision-making and precise control over crop management practices.

The ultimate goal of a smart crop monitoring system is to optimize resource utilization, improve crop productivity, and reduce costs by providing farmers with accurate and timely information for decision-making. By leveraging technology, such systems enable more efficient and sustainable crop management practices in modern agriculture.

The importance of a remote crop monitoring system

Among the most important tasks that are always done for good yields is the monitoring of crops. Since plants are constantly monitored, it ensures that they grow in the best conditions, and in case of any anomalies, it is corrected on time hence reducing the devastating impending impact.

As a farmer or agriculture enthusiast, it is worth noting that it is currently a must-do for one to expect better massive harvests and those that are of higher quality since most of the drawbacks are settled early enough.

Since monitoring crops is one of the pillar requirements for a good harvest, one needs to go for special training. Special training doesn’t mean one needs to hold a master’s or bachelor’s degree but only needs to understand how to coordinate, monitor and even weigh the obtained results. Through this, you will be able to make better decisions based on accurate diagnosis and later best quality yields.

When choosing to monitor your crops, you need to know that apart from prevention of infestations and spread of pests, diseases and even weeds are always under control hence no devastating effects that lower the performance and even the quality of the final products.

Are you aware that crops are always exposed to strange threats yearly due to mutations and transformations in the biological components of the pests hence usually choosing one similar method of treating them means you are mistaken and need to change tactics every time?

For that reason, monitoring crops is perceived as a serious task that needs more responsibility and one that should not be degraded.

Whenever one is monitoring fruit crops such as pears and even apples, when using an Integrated Pest Management Programme, it is advisable not only to track changes in trees but also to check on the weather in the area that affects them.

This enables you to have a list of possible pests that may pose a threat to the growth of trees. Using systematic visual monitoring of the orchard block will work whenever you want to reduce the cost and your time from planting season to the harvesting season.

Climate and pests

The questionable part when analyzing the influence of climate is simply that some pests can feed on it and later be toxic to agents to crops so fast. Basically in agriculture, many farmers always lack awareness hence noticing when it is too late when their crops are already massively infested by pests.

The better part of this is that pests always do react predictably to the climate hence a perfect strategy can be hatched to avoid another pest attack and also prevent them in the future.

Despite monitoring being done more frequently, vegetable and fruit farmers always get to know the presence of pests or any threat a little late. This shows how important it is to monitor climatic factors that eventually turn out to be earlier signs of the emergence and pest infestation.

Block crop monitoring

Many ways can be used to monitor crops, and one of them is through visual monitoring using blocks that enable you to analyze trees that have similar characteristics based on their variety, age, and, even physical condition.

The idea behind visual monitoring is to have blocks that act as signs and those that can be studied like they were a unit and not separately since it is a way that is used by several farmers across the globe and that horticultural experts always have limited time for them to review each of the blocks arranged in the field.

importance of a remote crop monitoring system

Certain attention needs to be paid during the selection of the most appropriate block. This is because the block that is chosen needs to have all the history of the pests so that the best treatment and prevention can be applied to protect the growth of other trees.

You do not have to have large equipment, tools, and even complex methods to monitor your crops. One of the best ways to go is simply to do a meticulous and extensive visual examination that enables you to notice the different types of pests present in the trees.

This can be always completed using a common lens, however, experts will always use more complex equipment such as a binocular microscope. This enables them to count and even identify mites and thrips.

Temperature of crops

For one to accurately measure the temperature when monitoring crops, he or she can choose to use a simple thermometer after finding out about simple information. Besides that, you may also choose to use a maximum to a minimum thermometer that is very common among rural suppliers and record extra information.

It is also worth noting that the thermometer needs to be placed on the orchard and ensure that it is not exposed to direct sunlight. You may also add a data logger to record more accurate weather information.

For data loggers, you may also choose to use those that have the potential of measuring the temperature, rain, humidity, and even the humidity of the leaves.

For you to put aside trees that will not be studied with those in the blocks that will act as indicators, for you to be able to differentiate them, you simply need to mark them using acrylic paint or even adhesive tape.

Application of an Integrated Pest Management vouches for selecting and marking flowers, buds, and fruits randomly to monitor each one of the trees.

After about the duration of one to two weeks, at the time that the fruit is developing, farmers need to spend ten to twenty minutes for every two and a half acres strictly managing the fruits, flowers, and buds that are marked searching for any signs that may show the presence of one or even more pests in the crop.

Whenever a pest is found in the crops, it needs to be recorded fast and also in a detailed way in the log made exactly for this kind of data.

Conclusion

All our expert team of agronomists are professionals in a range of branches of agriculture and are also capable of providing monitoring services through merging field visits with quantitative and qualitative assessment and agriculture remote crop monitoring system.

All these innovative systems are assessments that are focused on a wide range of activities that carefully monitor crop developments, gathering data and information that is related to the area.

This data is then carefully analyzed by our professional team and then noted in a final report that enables them to mention and evaluate major factors and factors that are capable of affecting the productivity of the crops.

Based on these results GeoPard is then able to give you an estimated time for harvesting, and also crop yield. This enables clients to smoothly monitor their crops through the recent satellite imagery. Weight data layers in a given place without using a given facility.

Comparison of Data Layers To Make Decisions About Crops

To visualize field data and make informed decisions it is often necessary to compare layers on multiple synchronized views.

In GeoPard, you can visually compare up to four layers of data on one screen. All layers work synchronously when you zoom in/out or move the map for your convenience.

How do I enter split-screen mode? Select a field and click the layer comparison icon in the upper right corner of the screen. Then select any snapshots, field control areas, or other layers that you want to see on the same screen at the same time. Click Compare Layers. 

The layer comparison feature synchronizes maps, cursors, zoom levels. Also you have the ability to add/remove layers. Currently we support up to 4 data layers.

What are data layers in precision agriculture?

In precision agriculture, data layers refer to the different types of data that are collected and analyzed to make informed decisions about crop management. These layers may include:

  • Soil data: Information about soil characteristics, such as nutrient levels, pH, and texture, which can inform decisions about fertilization and other soil management practices.
  • Weather data: Data on current and historical weather patterns, including temperature, precipitation, and wind, can help farmers make decisions about planting, irrigation, and other practices.
  • Crop data: Data on crop growth and health, including plant height, leaf area, and chlorophyll levels, can inform decisions about fertilization, irrigation, and pest management.
  • Yield data: Information on crop yield and quality can help farmers make decisions about harvesting and marketing their crops.
  • Topographic data: Information about the shape and elevation of the land can inform decisions about planting and irrigation, and can help identify areas that may be prone to erosion or other environmental problems.
  • Remote sensing data: Data collected from satellites, drones, or other remote sensors can provide information on crop health, soil moisture, and other factors that can inform decisions about crop management.

By analyzing and integrating these different data layers, farmers can make more informed decisions about crop management, leading to more efficient and sustainable farming practices.

Use of Cropped Raster Data for Agribusiness

Cropped raster data based on field boundary sounds very simple. Some data sources are rasters with pixels and a resolution of 3m/10m/30m, others – are vectors with polygons or multipolygons.

The accurate boundary of the cropped raster data is tricky. The default output most gis and precision agriculture software have is a pixelated raster. Precise data evaluation close to the field boundary helps you better to understand, for example, canopy conditions, slope value. 

Examples of pixelated rasters:

Near-infrared pixelated raster
Near-infrared pixelated raster
Elevation pixelated raster
Elevation pixelated raster

Is it possible to improve and to make it more accurate?

Yes, GeoPard does that and even makes data available for further integration via API. Some examples:

  • Crop of raw (RedGreenBlue and NearInfrared views) satellite imagery based on field boundary:
RGB cropped raster
GeoPard Agriculture RGB cropped raster
Near-infrared cropped raster
GeoPard Agriculture Near-infrared cropped raster

 

  • Crop of satellite imagery with vegetation index like WDRVI based on the field boundary:

 

WDRVI cropped raster
GeoPard Agriculture WDRVI cropped raster

 

  • Crop of digital topography dataset (elevation and roughness) based on the field boundary:

 

Elevation cropped raster
GeoPard Agriculture Elevation cropped raster
Roughness cropped raster
GeoPard Agriculture Roughness cropped raster

How it looks in GeoPard interface and how it can be integrated in your ag tech solution:

GeoPard Agriculture NIR cropped raster
GeoPard Agriculture NIR cropped raster
GeoPard Agriculture Relief Position
GeoPard Agriculture Relief Position
GeoPard Agriculture WDRVI
GeoPard Agriculture WDRVI

We at GeoPard understand the value of such details and are constantly working to improve the solution.

What is Raster Data?

Raster data is a type of digital image data that is represented by a grid of pixels or cells, where each cell corresponds to a specific location on the earth’s surface. Each pixel in a raster image is assigned a value that represents a particular attribute or characteristic of that location, such as elevation, temperature, or land cover.

It is commonly used in geographic information systems (GIS) and remote sensing applications to represent and analyze various types of spatial data. It can be collected from a variety of sources, including satellite and aerial imagery, digital cameras, and ground-based sensors.

It is often stored in various formats, such as GeoTIFF, JPEG, and PNG, which are designed to compress and store the data efficiently. GIS software and image processing tools can be used to manipulate and analyze data, such as by performing calculations on the pixel values or applying filters to enhance certain features.

Examples of applications include mapping land use and land cover, analyzing changes in vegetation over time, and predicting crop yields based on environmental factors.

How Raster Data Used In Precision Agriculture?

It is an essential component of precision agriculture, as it provides detailed information about crop health, soil properties, and environmental factors that can be used to make more informed decisions about crop management. Here are some examples of how raster data is used in precision agriculture:

  • Crop health analysis: Remote sensing data in the form of satellite imagery or drone imagery can be used to generate data layers that show vegetation indices such as NDVI (Normalized Difference Vegetation Index) or NDRE (Normalized Difference Red Edge). These indices help identify areas of the field with healthy vegetation, as well as areas where crops may be under stress due to disease, pests, or nutrient deficiencies.
  • Soil analysis: Soil data, such as soil moisture content or soil texture, can be collected using sensors that generate data layers. These layers can help identify areas of the field with varying soil characteristics, which can inform decisions about fertilization, irrigation, and other soil management practices.
  • Environmental analysis: Data layers that show environmental factors such as temperature, precipitation, and wind speed can be used to model crop growth and predict yield. These layers can also help identify areas of the field that are prone to erosion, flooding, or other environmental problems.
  • Variable rate application: It can be used to generate prescription maps for variable rate application of inputs such as fertilizer or pesticides. By applying inputs at varying rates according to the needs of different areas of the field, farmers can reduce waste and optimize crop growth.

Overall, raster data is a crucial tool in precision agriculture, as it provides detailed information about crop and soil conditions that can be used to make more informed decisions about crop management.

Which vegetation index is better to use in Precision Agriculture?

There are several vegetation indices that are commonly used, including the Normalized Difference Vegetation Index (NDVI), Wide Dynamic Range Vegetation Index (WDRVI), and Green Chlorophyll Index (GCI).

  • Which vegetation index reflects more details?
  • Which vegetation index shows variation better?
  • Is NDVI the best in the multispectral vegetation index family?

The questions are known and coming up very often. Let’s investigate.

What is vegetation index?

Vegetation index is a numerical measure that quantifies the amount and condition of vegetation in a specific area based on remote sensing data.

Vegetation indices are calculated by combining different spectral bands from satellite imagery or aerial photography, which reflect the amount of energy absorbed and reflected by plants in the visible and near-infrared regions of the electromagnetic spectrum.

These indices can provide information about the health, density, and productivity of vegetation, which is useful for a wide range of applications, including agriculture, forestry, land management, and climate monitoring.

What is Normalized Difference Vegetation Index (NDVI)?

NDVI (Normalized Difference Vegetation Index) is the most famous and widely used in industries related to biomass and remote sensing.

NDVI saturation affects the accurate distinguishing of vegetation at biomass peaks. Another issue with NDVI is the soil noise effect on the early stages of crop development.

It is calculated using satellite or aerial remote sensing data, based on the difference in the reflectance of two spectral bands: the near-infrared (NIR) and the red band.

The NDVI formula is NDVI = (NIR-Red) / (NIR+Red).

Where NIR is the reflectance in the near-infrared band and Red is the reflectance in the red band.

The resulting NDVI value ranges from -1 to +1, with higher values indicating a higher density of vegetation. A value of zero indicates no vegetation, while negative values indicate water bodies or other non-vegetated surfaces.

NDVI values close to +1 indicate dense and healthy vegetation, while values closer to zero indicate sparse vegetation or areas with significant stress or damage.

It is widely used in agricultural and ecological applications to monitor vegetation growth, estimate crop yields, and assess the health and productivity of forests and other ecosystems.

It can also be used to detect and monitor drought, soil erosion, and other environmental factors that affect vegetation cover.

It, for example, is calculated by subtracting the reflectance in the near-infrared (NIR) band from the reflectance in the red band and dividing the result by the sum of the two. The resulting value ranges from -1 to +1, with higher values indicating higher levels of vegetation.

Furthermore, the idea of WDRVI (Wide Dynamic Range Vegetation Index) was created to resolve NDVI saturation issues. It was reached by expanding the range of possible WDRVI values via the introduction of the mathematical coefficient (α).

NDVI (Normalized Difference Vegetation Index) use

The NDVI (normalized difference vegetation index) formula was transformed into WDRVI = (α∗NIR-Red) / (α∗NIR+Red).

WDRVI (Wide Dynamic Range Vegetation Index) and NDVI

Zones built based on WDRVI are better compared to NDVI zones. Nevertheless, they are still not ideal because of too high biomass. 

GCI (Green Chlorophyll Index) is used to estimate leaf chlorophyll content in the plants based on near-infrared and green bands. In general, the chlorophyll value directly reflects the vegetation.

The GCI formula looks like GCI = NIR / Green – 1.

GCI (Green Chlorophyll Index) 

Zones built based on GCI better distinguish high biomass spots compared to NDVI and WDRVI. The details help to manage the field more accurately and efficiently.

RCI (Red Chlorophyll Index) incorporates the same chlorophyll content knowledge base as GCI and reflects it via the red multispectral band.

The RCI formula looks like RCI = NIR / Red – 1.

RCI (Red Chlorophyll Index) 

Zones based on RCI are accurate as GCI zones.

Keep tracking your fields and utilize the right vegetation index at the right moment during the season. A large family of vegetation indices is available in GeoPard right now.

Agriculture Zones Operations For Data-Driven Decision

ZONES OPERATIONS ACROSS DIFFERENT LAYERS

In precision farming, field data collection and data-driven decision making are absolutely essential. As the next stage in the development of multi-layers analytics and finding dependencies across layers we introduce new module Zones Operations. 

There you can search for dependencies between different ag zones maps such as historical vegetation, topography including its derivatives, data from yield monitors, soil data, scanners, stability maps, and so on. This is a step forward in defining the most influenced areas and understanding the reasons for field heterogeneity. 

How can you identify the areas? 

First of all select field maps, you want to cross-investigate. A layer comparison view is a good approach to define specific agriculture zones for analyzing.

You may want to compare low yield potential and sloppy areas, most unstable zones and low vegetation, low electrical conductivity and yield, as applied fertilization map and current vegetation, and others.

Secondly, mark specific agriculture zones on every map you want to compare in the Zones Operations module. And finally, obtain a zone of interest. Note that it is possible to use more than two maps in analyses. 

How can you apply this knowledge? 

In addition to finding relationships that can help explain yield, it is possible to set yield goals for defined agriculture zones; to scout interesting areas; to reduce investments in such localized zones or build the plan of mitigating limiting factors and pull up underperforming areas knowing the underlying causes; to build agronomy plan using VRA practices. 

There are several examples of field insights on screenshots. Note that every field is unique and below-mentioned cases do not guarantee 100% same result for your field but it is a good way to start the investigation from. 

You are very welcome to share your agronomic practices by commenting on this post, contact the GeoPard Agriculture team directly. We are open to the feedback because we build the solution for you for a better understanding of field variability and managing it.

Zones Quality

Almost all management zones are adjusted before becoming a Variable Rate Application map. This can be merging some zones together, manual corrections in well-known spots, the addition of extra buffer areas, ag equipment compatibility, etc.

We, in the GeoPard team, understand that accurate natural management zones with valid polygons will save a lot of time during zone verification and correction processes.

The GeoPard engine does the following:

  • automatically removes noise,
  • automatically merges small polygons into the closest bigger zone,
  • keeps only the necessary minimum amount of points in every zone polygon,
  • makes VRA maps compatible with any agricultural equipment and machinery.

In addition to automatic correction, the tool to merge and split zones is available to adjust the map according to your own field knowledge and agronomic practice. 

There are many different maps from various providers on the market, but you will definitely recognize GeoPard maps.

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