{"id":11785,"date":"2025-07-06T21:42:54","date_gmt":"2025-07-06T19:42:54","guid":{"rendered":"https:\/\/geopard.tech\/?p=11785"},"modified":"2025-07-06T21:48:35","modified_gmt":"2025-07-06T19:48:35","slug":"remote-sensing-vegetation-indices-transform-potato-yield-forecasting","status":"publish","type":"post","link":"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting"},"content":{"rendered":"<p>Potato stands as one of the world\u2019s most important food crops, serving as a staple for millions of people. Firstly, knowing how potato plants grow and being able to predict their yield helps farmers manage irrigation, fertilization, and pest control more effectively.<\/p>\n<p>Secondly, food processors and storage facilities can better plan logistics and labor when they have reliable yield estimates. However, traditional methods\u2014such as physically walking through fields and measuring plants by hand\u2014are time\u2011consuming and prone to human error.<\/p>\n<p>Therefore, scientists have turned to remote sensing, which uses cameras and sensors on satellites, drones, or handheld devices, to monitor potato growth and forecast yield more rapidly and accurately.<\/p>\n<h2>Understanding Potato Yield Forecasts<\/h2>\n<p>Over the past two decades, interest in applying remote sensing to potato research has grown substantially. In fact, a systematic review identified 79 studies published between 2000 and 2022 on this topic, out of 482 initially screened articles.<\/p>\n<p>To ensure transparency and reproducibility, the authors followed established guidelines (Kitchenham &amp; Charters 2007; PRISMA framework), searching eight major databases\u2014Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis, and SpringerLink\u2014using terms like \u201cpotato yield prediction\u201d AND \u201cremote sensing.\u201d<\/p>\n<p>Consequently, only original research in English that used remote sensing data for growth monitoring or yield estimation was included. Moreover, data from each selected paper were extracted according to four key questions:<\/p>\n<ul>\n<li>Which sensing platform was used (satellite, UAV, or ground\u2011based)?<\/li>\n<li>Which vegetation indices or spectral features were evaluated?<\/li>\n<li>Which crop traits were monitored (biomass, leaf area, chlorophyll, nitrogen)?<\/li>\n<li>How accurately could final tuber yield be predicted (coefficient of determination, R\u00b2)?<\/li>\n<\/ul>\n<p>These questions helped the reviewers map out the state of the art and identify gaps where future research could focus.<\/p>\n<h2>Remote Sensing Platforms and Vegetation Indices<\/h2>\n<p>Researchers have employed three main types of remote sensing platforms, each with its own advantages and limitations. Firstly, optical satellites such as Sentinel\u20112 (10\u202fm spatial resolution, 5\u202fday revisit) and Landsat 5\u20138 (30\u202fm, 16\u202fday revisit) offer broad coverage and often free data access.<\/p>\n<p>Secondly, satellites like MODIS\/TERRA\/Aqua (250\u20131000\u202fm, daily to 2\u202fday revisit) and commercial systems like PlanetScope (3\u202fm, daily, costing about $218 per 100\u202fkm\u00b2) allow for more frequent or higher\u2011resolution monitoring, although costs can be a factor.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/remote-sensing-platforms-and-vegetation-indices\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Remote Sensing Platforms and Vegetation Indices\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11792\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=810%2C810&#038;ssl=1\" alt=\"Remote Sensing Platforms and Vegetation Indices\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>Thirdly, unmanned aerial vehicles (UAVs) carrying multispectral or hyperspectral cameras provide very high resolution (down to a few centimeters per pixel) and can be flown on demand, but they cover smaller areas and require more logistics.<\/p>\n<p>Finally, ground\u2011based sensors\u2014such as handheld NDVI meters and SPAD chlorophyll meters\u2014give spot measurements that are highly precise, although they are time\u2011intensive when used over large fields.<\/p>\n<p>Vegetation indices (VIs) translate raw reflectance values into meaningful estimates of plant traits. The most common indices in potato studies include:<\/p>\n<ul>\n<li>NDVI (Normalized Difference Vegetation Index): (NIR \u2013 Red) \/ (NIR + Red)<\/li>\n<li>GNDVI (Green NDVI): (NIR \u2013 Green) \/ (NIR + Green)<\/li>\n<li>NDRE (Normalized Difference Red\u2011Edge): (NIR \u2013 RedEdge) \/ (NIR + RedEdge)<\/li>\n<li>OSAVI (Optimized Soil\u2011Adjusted Vegetation Index): 1.16 \u00d7 (NIR \u2013 Red) \/ (NIR + Red + 0.16)<\/li>\n<li>EVI (Enhanced Vegetation Index), CIred\u2011edge, CIgreen, and more .<\/li>\n<\/ul>\n<p>These indices are chosen based on their sensitivity to canopy cover, chlorophyll content, and soil background. Consequently, they serve as the foundation for estimating plant health and predicting yield.<\/p>\n<h2>Potato Monitoring Growth and Predicting Yield<\/h2>\n<p>Through remote sensing, researchers monitor key potato crop traits\u2014aboveground biomass (AGB), leaf area index (LAI), canopy chlorophyll content (CCC), and leaf nitrogen status\u2014and then relate these to final tuber yield.<\/p>\n<p>Firstly, estimating AGB using VIs alone can be challenging when canopy cover is dense because many indices saturate; therefore, combining VIs with plant height or texture features in machine\u2011learning models often improves accuracy.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/potato-monitoring-growth-and-predicting-yield\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Potato Monitoring Growth and Predicting Yield\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11793\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=810%2C810&#038;ssl=1\" alt=\"Potato Monitoring Growth and Predicting Yield\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>Secondly, assessing LAI\u2014the total one\u2011sided leaf area per ground area\u2014has achieved R\u00b2 values up to 0.84 by using time\u2011series data from both UAV hyperspectral and satellite multispectral sensors.<\/p>\n<p>Thirdly, CCC estimates, derived from indices like CIred\u2011edge, CIgreen, TCARI\/OSAVI, and TCARI\u202f+\u202fOSAVI, reached R\u00b2 \u2248 0.85 during the vegetative stage, indicating strong correlation with lab\u2011measured chlorophyll.<\/p>\n<p>Lastly, leaf nitrogen status, vital for healthy growth, was predicted with R\u00b2 ranging from 0.52 to 0.95 when using ground\u2011based sensors plus regression or random\u2011forest models.<\/p>\n<p>When it comes to tuber yield prediction, two main modeling approaches stand out:<\/p>\n<p>Empirical Regression Models: Here, a single VI\u2014most often NDVI, GNDVI, or NDRE\u2014is fitted to ground\u2011truth yield data. Reported R\u00b2 values for NDVI vs. yield range from 0.23 to 0.84 (median \u2248 0.67), while NDRE\u2013yield correlations range from 0.12 to 0.85 (median \u2248 0.61).<\/p>\n<p>Machine\u2011Learning Models: These include random forest, support vector machines, and neural networks that combine multiple VIs, spectral bands, and non\u2011spectral factors such as weather, soil, and management. Such models have pushed R\u00b2 up to 0.93 in some studies.<\/p>\n<p>Moreover, the timing of data collection greatly affects prediction accuracy. Across multiple studies, VI measurements taken at 36\u201355 days after planting (DAP) yielded the highest correlations with final tuber yield.<\/p>\n<p>This stage aligns with maximum ground cover and the onset of tuber initiation, making plant structure most indicative of eventual yield. Some of the key statistics found:<\/p>\n<ul>\n<li>79 studies (2000\u20132022) met the review criteria, out of 482 identified.<\/li>\n<li>Focus areas: yield prediction (37\u202f%), leaf N status (21\u202f%), AGB (15\u202f%), LAI (15\u202f%), CCC (12\u202f%).<\/li>\n<li>Satellite platforms most used: Sentinel\u20112, Landsat, MODIS; commercial: PlanetScope.<\/li>\n<li>R\u00b2 ranges: NDVI\u2013yield (0.23\u20130.84), NDRE\u2013yield (0.12\u20130.85), GNDVI\u2013yield (0.26\u20130.75).<\/li>\n<\/ul>\n<h2>Potato Yield Prediction Recommendations<\/h2>\n<p>Based on these findings, practitioners should first select the appropriate platform for their goals. For regional yield forecasts, free Sentinel\u20112 data provide reliable coverage with 10\u202fm resolution and a 5\u202fday revisit schedule.<\/p>\n<p>To refine local estimates, UAV flights scheduled around 36\u201355 days after planting capture critical canopy dynamics and improve calibration of satellite models. Ground sensors are best used for spot checks and to calibrate remote observations, especially when combining spectral data with field measurements.<\/p>\n<p>In terms of vegetation indices, practitioners should prioritize NDVI, NDRE, and CI&lt;sub&gt;red\u2011edge&lt;\/sub&gt; for predicting final yield, as these consistently show strong correlations.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/potato-yield-prediction-recommendations\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"Potato Yield Prediction Recommendations\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11794\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=810%2C810&#038;ssl=1\" alt=\"Potato Yield Prediction Recommendations\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>When estimating chlorophyll and nitrogen content, combining red\u2011edge indices with soil\u2011adjusted VIs\u2014such as TCARI\/OSAVI\u2014yields the most accurate results. For biomass estimation, integrating VIs with plant height or texture features within machine\u2011learning frameworks further boosts accuracy.<\/p>\n<p>As for modeling, simple linear or non\u2011linear regressions using a single index are effective when ground\u2011truth data are limited. However, when multiple indices and ancillary data (weather, soil, management) are available, machine\u2011learning methods such as random forest or neural networks offer superior performance. Importantly, timing imagery around 36\u201355 days after planting is crucial, as this window consistently delivers the highest prediction accuracy.<\/p>\n<h2>Conclusion<\/h2>\n<p>In conclusion, remote sensing offers a fast, flexible, and accurate toolkit for monitoring potato growth and predicting tuber yield. By choosing the appropriate platform, selecting the most informative vegetation indices, timing data collection around 36\u201355\u202fDAP, and applying suitable modeling techniques, researchers and practitioners can significantly improve yield forecasts.<\/p>\n<p>This approach not only saves time but also supports smarter management decisions, ultimately benefiting farmers, agronomists, and the entire potato supply chain.<\/p>\n<p><strong>Reference<\/strong>: Mukiibi, A., Machakaire, A.T.B., Franke, A.C.\u00a0<i>et al.<\/i>\u00a0A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing.\u00a0<i>Potato Res.<\/i>\u00a0<b>68<\/b>, 409\u2013448 (2025). <a href=\"https:\/\/doi.org\/10.1007\/s11540-024-09748-7\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s11540-024-09748-7<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Potato stands as one of the world\u2019s most important food crops, serving as a staple for millions of people. Firstly, knowing how potato plants grow&#8230;<\/p>","protected":false},"author":210157960,"featured_media":11791,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1378],"tags":[],"class_list":["post-11785","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-remote-sensing"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/\" \/>\n<meta property=\"og:locale\" content=\"nn_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture\" \/>\n<meta property=\"og:description\" content=\"Potato stands as one of the world\u2019s most important food crops, serving as a staple for millions of people. Firstly, knowing how potato plants grow...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/\" \/>\n<meta property=\"og:site_name\" content=\"GeoPard - Precision agriculture Mapping software\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/geopardAgriculture\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-06T19:42:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-06T19:48:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"dementievgeopard\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@geopardagri\" \/>\n<meta name=\"twitter:site\" content=\"@geopardagri\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"dementievgeopard\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutt\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\"},\"author\":{\"name\":\"dementievgeopard\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/person\\\/dd217733c742620adc57befbbcd84a8a\"},\"headline\":\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting\",\"datePublished\":\"2025-07-06T19:42:54+00:00\",\"dateModified\":\"2025-07-06T19:48:35+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\"},\"wordCount\":1197,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1\",\"articleSection\":[\"Remote Sensing\"],\"inLanguage\":\"nn-NO\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\",\"url\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\",\"name\":\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1\",\"datePublished\":\"2025-07-06T19:42:54+00:00\",\"dateModified\":\"2025-07-06T19:48:35+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#breadcrumb\"},\"inLanguage\":\"nn-NO\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"nn-NO\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#primaryimage\",\"url\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1\",\"contentUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1\",\"width\":1920,\"height\":1080,\"caption\":\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/geopard.tech\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#website\",\"url\":\"https:\\\/\\\/geopard.tech\\\/\",\"name\":\"GeoPard - Precision agriculture software\",\"description\":\"Precision agriculture Mapping software\",\"publisher\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\"},\"alternateName\":\"GeoPard\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/geopard.tech\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"nn-NO\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\",\"name\":\"GeoPard Agriculture\",\"alternateName\":\"GeoPard\",\"url\":\"https:\\\/\\\/geopard.tech\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"nn-NO\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2022\\\/03\\\/favicon.png?fit=200%2C200&ssl=1\",\"contentUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2022\\\/03\\\/favicon.png?fit=200%2C200&ssl=1\",\"width\":200,\"height\":200,\"caption\":\"GeoPard Agriculture\"},\"image\":{\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/geopardAgriculture\\\/\",\"https:\\\/\\\/x.com\\\/geopardagri\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/geopard-agriculture\\\/\",\"https:\\\/\\\/www.instagram.com\\\/geopardagriculture\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/person\\\/dd217733c742620adc57befbbcd84a8a\",\"name\":\"dementievgeopard\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"nn-NO\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g\",\"caption\":\"dementievgeopard\"},\"url\":\"#\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","og_locale":"nn_NO","og_type":"article","og_title":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture","og_description":"Potato stands as one of the world\u2019s most important food crops, serving as a staple for millions of people. Firstly, knowing how potato plants grow...","og_url":"https:\/\/geopard.tech\/nor\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","og_site_name":"GeoPard - Precision agriculture Mapping software","article_publisher":"https:\/\/www.facebook.com\/geopardAgriculture\/","article_published_time":"2025-07-06T19:42:54+00:00","article_modified_time":"2025-07-06T19:48:35+00:00","og_image":[{"width":1920,"height":1080,"url":"https:\/\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png","type":"image\/png"}],"author":"dementievgeopard","twitter_card":"summary_large_image","twitter_creator":"@geopardagri","twitter_site":"@geopardagri","twitter_misc":{"Written by":"dementievgeopard","Est. reading time":"6 minutt"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#article","isPartOf":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/"},"author":{"name":"dementievgeopard","@id":"https:\/\/geopard.tech\/#\/schema\/person\/dd217733c742620adc57befbbcd84a8a"},"headline":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting","datePublished":"2025-07-06T19:42:54+00:00","dateModified":"2025-07-06T19:48:35+00:00","mainEntityOfPage":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/"},"wordCount":1197,"commentCount":0,"publisher":{"@id":"https:\/\/geopard.tech\/#organization"},"image":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#primaryimage"},"thumbnailUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1","articleSection":["Remote Sensing"],"inLanguage":"nn-NO","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","url":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","name":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture","isPartOf":{"@id":"https:\/\/geopard.tech\/#website"},"primaryImageOfPage":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#primaryimage"},"image":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#primaryimage"},"thumbnailUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1","datePublished":"2025-07-06T19:42:54+00:00","dateModified":"2025-07-06T19:48:35+00:00","breadcrumb":{"@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#breadcrumb"},"inLanguage":"nn-NO","potentialAction":[{"@type":"ReadAction","target":["https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/"]}]},{"@type":"ImageObject","inLanguage":"nn-NO","@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#primaryimage","url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1","contentUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1","width":1920,"height":1080,"caption":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting"},{"@type":"BreadcrumbList","@id":"https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/geopard.tech\/"},{"@type":"ListItem","position":2,"name":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting"}]},{"@type":"WebSite","@id":"https:\/\/geopard.tech\/#website","url":"https:\/\/geopard.tech\/","name":"GeoPard - Precision agriculture software","description":"Precision agriculture Mapping software","publisher":{"@id":"https:\/\/geopard.tech\/#organization"},"alternateName":"GeoPard","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/geopard.tech\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"nn-NO"},{"@type":"Organization","@id":"https:\/\/geopard.tech\/#organization","name":"GeoPard Agriculture","alternateName":"GeoPard","url":"https:\/\/geopard.tech\/","logo":{"@type":"ImageObject","inLanguage":"nn-NO","@id":"https:\/\/geopard.tech\/#\/schema\/logo\/image\/","url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/03\/favicon.png?fit=200%2C200&ssl=1","contentUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/03\/favicon.png?fit=200%2C200&ssl=1","width":200,"height":200,"caption":"GeoPard Agriculture"},"image":{"@id":"https:\/\/geopard.tech\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/geopardAgriculture\/","https:\/\/x.com\/geopardagri","https:\/\/www.linkedin.com\/company\/geopard-agriculture\/","https:\/\/www.instagram.com\/geopardagriculture\/"]},{"@type":"Person","@id":"https:\/\/geopard.tech\/#\/schema\/person\/dd217733c742620adc57befbbcd84a8a","name":"dementievgeopard","image":{"@type":"ImageObject","inLanguage":"nn-NO","@id":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/7c09adac9c20b00454199df5a41ea67c812a6c97e948bf109899836ddde31354?s=96&d=identicon&r=g","caption":"dementievgeopard"},"url":"#"}]}},"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pdiCPa-345","jetpack_featured_media_url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Vegetation-Indices-Transform-Potato-Yield-Forecasting.png?fit=1920%2C1080&ssl=1","_links":{"self":[{"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/posts\/11785","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/users\/210157960"}],"replies":[{"embeddable":true,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/comments?post=11785"}],"version-history":[{"count":0,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/posts\/11785\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/media\/11791"}],"wp:attachment":[{"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/media?parent=11785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/categories?post=11785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geopard.tech\/nor\/wp-json\/wp\/v2\/tags?post=11785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}