{"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":"les-indices-de-vegetation-issus-de-la-teledetection-transforment-les-previsions-de-rendement-de-la-pomme-de-terre","status":"publish","type":"post","link":"https:\/\/geopard.tech\/fr\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Les indices de v\u00e9g\u00e9tation issus de la t\u00e9l\u00e9d\u00e9tection transforment les pr\u00e9visions de rendement de la pomme de terre"},"content":{"rendered":"<p>La pomme de terre est l&#039;une des cultures vivri\u00e8res les plus importantes au monde, constituant un aliment de base pour des millions de personnes. Tout d&#039;abord, la connaissance de la croissance des plants de pommes de terre et la capacit\u00e9 de pr\u00e9voir leur rendement permettent aux agriculteurs de g\u00e9rer plus efficacement l&#039;irrigation, la fertilisation et la lutte antiparasitaire.<\/p>\n<p>Deuxi\u00e8mement, les entreprises de transformation alimentaire et les installations de stockage peuvent mieux planifier la logistique et la main-d&#039;\u0153uvre lorsqu&#039;elles disposent d&#039;estimations de rendement fiables. Cependant, les m\u00e9thodes traditionnelles, telles que l&#039;inspection visuelle des champs et la mesure manuelle des plantes, sont chronophages et sujettes \u00e0 l&#039;erreur humaine.<\/p>\n<p>C\u2019est pourquoi les scientifiques se sont tourn\u00e9s vers la t\u00e9l\u00e9d\u00e9tection, qui utilise des cam\u00e9ras et des capteurs embarqu\u00e9s sur des satellites, des drones ou des appareils portables, pour surveiller la croissance des pommes de terre et pr\u00e9voir les rendements plus rapidement et avec plus de pr\u00e9cision.<\/p>\n<h2>Comprendre les pr\u00e9visions de rendement des pommes de terre<\/h2>\n<p>Au cours des deux derni\u00e8res d\u00e9cennies, l&#039;int\u00e9r\u00eat pour l&#039;application de la t\u00e9l\u00e9d\u00e9tection \u00e0 la recherche sur la pomme de terre a consid\u00e9rablement augment\u00e9. En effet, une revue syst\u00e9matique a recens\u00e9 79 \u00e9tudes publi\u00e9es entre 2000 et 2022 sur ce sujet, parmi 482 articles initialement s\u00e9lectionn\u00e9s.<\/p>\n<p>Pour garantir la transparence et la reproductibilit\u00e9, les auteurs ont suivi les directives \u00e9tablies (Kitchenham &amp; Charters 2007 ; cadre PRISMA), en effectuant des recherches dans huit bases de donn\u00e9es majeures \u2014 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis et SpringerLink \u2014 en utilisant des termes comme \u201c pr\u00e9diction du rendement de la pomme de terre \u201d ET \u201c t\u00e9l\u00e9d\u00e9tection \u201d.\u201d<\/p>\n<p>Par cons\u00e9quent, seules les recherches originales en anglais utilisant des donn\u00e9es de t\u00e9l\u00e9d\u00e9tection pour le suivi de la croissance ou l&#039;estimation du rendement ont \u00e9t\u00e9 incluses. De plus, les donn\u00e9es de chaque article s\u00e9lectionn\u00e9 ont \u00e9t\u00e9 extraites en fonction de quatre questions cl\u00e9s\u00a0:<\/p>\n<ul>\n<li>Quelle plateforme de d\u00e9tection a \u00e9t\u00e9 utilis\u00e9e (satellite, drone ou au sol)\u00a0?<\/li>\n<li>Quels indices de v\u00e9g\u00e9tation ou caract\u00e9ristiques spectrales ont \u00e9t\u00e9 \u00e9valu\u00e9s\u00a0?<\/li>\n<li>Quels param\u00e8tres des cultures ont \u00e9t\u00e9 suivis (biomasse, surface foliaire, chlorophylle, azote)\u00a0?<\/li>\n<li>Avec quelle pr\u00e9cision le rendement final en tubercules pourrait-il \u00eatre pr\u00e9dit (coefficient de d\u00e9termination, R\u00b2)\u00a0?<\/li>\n<\/ul>\n<p>Ces questions ont aid\u00e9 les examinateurs \u00e0 dresser un \u00e9tat des lieux et \u00e0 identifier les lacunes sur lesquelles les recherches futures pourraient se concentrer.<\/p>\n<h2>Plateformes de t\u00e9l\u00e9d\u00e9tection et indices de v\u00e9g\u00e9tation<\/h2>\n<p>Les chercheurs ont utilis\u00e9 trois principaux types de plateformes de t\u00e9l\u00e9d\u00e9tection, chacune pr\u00e9sentant ses propres avantages et limitations. Premi\u00e8rement, les satellites optiques tels que Sentinel-2 (r\u00e9solution spatiale de 10 m, revisite tous les 5 jours) et Landsat 5 \u00e0 8 (30 m, revisite tous les 16 jours) offrent une large couverture et souvent un acc\u00e8s gratuit aux donn\u00e9es.<\/p>\n<p>Deuxi\u00e8mement, les satellites comme MODIS\/TERRA\/Aqua (250\u20131000 m, revisite quotidienne \u00e0 2 jours) et les syst\u00e8mes commerciaux comme PlanetScope (3 m, quotidiennement, co\u00fbtant environ $218 par 100 km\u00b2) permettent une surveillance plus fr\u00e9quente ou \u00e0 plus haute r\u00e9solution, bien que les co\u00fbts puissent \u00eatre un facteur.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/fr\/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-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=\"Plateformes de t\u00e9l\u00e9d\u00e9tection et indices de v\u00e9g\u00e9tation\" 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>Troisi\u00e8mement, les v\u00e9hicules a\u00e9riens sans pilote (UAV) transportant des cam\u00e9ras multispectrales ou hyperspectrales offrent une tr\u00e8s haute r\u00e9solution (jusqu&#039;\u00e0 quelques centim\u00e8tres par pixel) et peuvent \u00eatre pilot\u00e9s \u00e0 la demande, mais ils couvrent des zones plus petites et n\u00e9cessitent une logistique plus importante.<\/p>\n<p>Enfin, les capteurs au sol, tels que les appareils de mesure NDVI portables et les chlorophylle-m\u00e8tres SPAD, fournissent des mesures ponctuelles tr\u00e8s pr\u00e9cises, bien qu&#039;elles soient chronophages lorsqu&#039;elles sont utilis\u00e9es sur de grands champs.<\/p>\n<p>Les indices de v\u00e9g\u00e9tation (IV) permettent de convertir les valeurs brutes de r\u00e9flectance en estimations pertinentes des caract\u00e9ristiques des plantes. Les indices les plus couramment utilis\u00e9s dans les \u00e9tudes sur la pomme de terre sont les suivants\u00a0:<\/p>\n<ul>\n<li>NDVI (Indice de v\u00e9g\u00e9tation par diff\u00e9rence normalis\u00e9e) : (NIR \u2013 Rouge) \/ (NIR + Rouge)<\/li>\n<li>GNDVI (Indice de v\u00e9g\u00e9tation par diff\u00e9rence de vert) : (NIR \u2013 Vert) \/ (NIR + Vert)<\/li>\n<li>NDRE (Diff\u00e9rence normalis\u00e9e du bord rouge) : (NIR \u2013 Bord rouge) \/ (NIR + Bord rouge)<\/li>\n<li>OSAVI (Indice de v\u00e9g\u00e9tation ajust\u00e9 au sol optimis\u00e9) : 1,16 \u00d7 (NIR \u2013 Rouge) \/ (NIR + Rouge + 0,16)<\/li>\n<li>EVI (Enhanced Vegetation Index), CIred-edge, CIgreen, et plus encore. .<\/li>\n<\/ul>\n<p>Ces indices sont choisis en fonction de leur sensibilit\u00e9 \u00e0 la couverture v\u00e9g\u00e9tale, \u00e0 la teneur en chlorophylle et aux caract\u00e9ristiques du sol. Ils servent ainsi de base \u00e0 l&#039;estimation de la sant\u00e9 des plantes et \u00e0 la pr\u00e9diction des rendements.<\/p>\n<h2>Surveillance de la croissance et pr\u00e9vision du rendement des pommes de terre<\/h2>\n<p>Gr\u00e2ce \u00e0 la t\u00e9l\u00e9d\u00e9tection, les chercheurs surveillent les principales caract\u00e9ristiques des cultures de pommes de terre \u2014 la biomasse a\u00e9rienne (AGB), l&#039;indice de surface foliaire (LAI), la teneur en chlorophylle du couvert (CCC) et l&#039;\u00e9tat de l&#039;azote foliaire \u2014 et les relient ensuite au rendement final en tubercules.<\/p>\n<p>Premi\u00e8rement, l&#039;estimation de la biomasse a\u00e9rienne \u00e0 l&#039;aide des seuls indices de v\u00e9g\u00e9tation peut s&#039;av\u00e9rer difficile lorsque la couverture v\u00e9g\u00e9tale est dense, car de nombreux indices atteignent la saturation ; par cons\u00e9quent, la combinaison des indices de v\u00e9g\u00e9tation avec la hauteur des plantes ou les caract\u00e9ristiques de texture dans les mod\u00e8les d&#039;apprentissage automatique am\u00e9liore souvent la pr\u00e9cision.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/fr\/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-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=\"Surveillance de la croissance et pr\u00e9vision du rendement des pommes de terre\" 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>Deuxi\u00e8mement, l\u2019\u00e9valuation de l\u2019indice LAI (surface foliaire totale unilat\u00e9rale par unit\u00e9 de surface au sol) a atteint des valeurs R\u00b2 allant jusqu\u2019\u00e0 0,84 en utilisant des donn\u00e9es de s\u00e9ries temporelles provenant \u00e0 la fois de capteurs hyperspectraux UAV et de capteurs multispectraux satellitaires.<\/p>\n<p>Troisi\u00e8mement, les estimations de CCC, d\u00e9riv\u00e9es d&#039;indices comme CIred\u2011edge, CIgreen, TCARI\/OSAVI et TCARI + OSAVI, ont atteint R\u00b2 \u2248 0,85 pendant la phase v\u00e9g\u00e9tative, indiquant une forte corr\u00e9lation avec la chlorophylle mesur\u00e9e en laboratoire.<\/p>\n<p>Enfin, l\u2019\u00e9tat de l\u2019azote foliaire, essentiel \u00e0 une croissance saine, a \u00e9t\u00e9 pr\u00e9dit avec un R\u00b2 allant de 0,52 \u00e0 0,95 en utilisant des capteurs au sol plus des mod\u00e8les de r\u00e9gression ou de for\u00eat al\u00e9atoire.<\/p>\n<p>En mati\u00e8re de pr\u00e9vision du rendement des tubercules, deux principales approches de mod\u00e9lisation se distinguent\u00a0:<\/p>\n<p>Mod\u00e8les de r\u00e9gression empiriques\u00a0: Dans ce type de mod\u00e8le, un seul indice de v\u00e9g\u00e9tation (le plus souvent NDVI, GNDVI ou NDRE) est ajust\u00e9 aux donn\u00e9es de rendement r\u00e9elles. Les valeurs de R\u00b2 rapport\u00e9es pour le NDVI par rapport au rendement varient de 0,23 \u00e0 0,84 (m\u00e9diane \u2248 0,67), tandis que les corr\u00e9lations NDRE-rendement varient de 0,12 \u00e0 0,85 (m\u00e9diane \u2248 0,61).<\/p>\n<p>Mod\u00e8les d&#039;apprentissage automatique\u00a0: Ces mod\u00e8les, tels que les for\u00eats al\u00e9atoires, les machines \u00e0 vecteurs de support et les r\u00e9seaux de neurones, combinent plusieurs indices de v\u00e9g\u00e9tation, bandes spectrales et facteurs non spectraux comme les conditions m\u00e9t\u00e9orologiques, le sol et les pratiques de gestion. Dans certaines \u00e9tudes, ces mod\u00e8les ont permis d&#039;atteindre un coefficient de d\u00e9termination (R\u00b2) de 0,93.<\/p>\n<p>De plus, le moment de la collecte des donn\u00e9es influe consid\u00e9rablement sur la pr\u00e9cision des pr\u00e9dictions. Dans plusieurs \u00e9tudes, les mesures de l&#039;indice de v\u00e9g\u00e9tation (IV) effectu\u00e9es entre 36 et 55 jours apr\u00e8s la plantation (JAP) ont donn\u00e9 les corr\u00e9lations les plus \u00e9lev\u00e9es avec le rendement final en tubercules.<\/p>\n<p>Ce stade co\u00efncide avec une couverture du sol maximale et le d\u00e9but de la tub\u00e9risation, ce qui rend la structure de la plante particuli\u00e8rement r\u00e9v\u00e9latrice du rendement final. Voici quelques statistiques cl\u00e9s\u00a0:<\/p>\n<ul>\n<li>79 \u00e9tudes (2000\u20132022) r\u00e9pondaient aux crit\u00e8res de s\u00e9lection, sur 482 identifi\u00e9es.<\/li>\n<li>Domaines d&#039;int\u00e9r\u00eat : pr\u00e9diction du rendement (37 %), statut N des feuilles (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Plateformes satellitaires les plus utilis\u00e9es\u00a0: Sentinel\u20112, Landsat, MODIS\u00a0; commerciale\u00a0: PlanetScope.<\/li>\n<li>Plages de R\u00b2 : NDVI\u2013rendement (0,23\u20130,84), NDRE\u2013rendement (0,12\u20130,85), GNDVI\u2013rendement (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Recommandations pour la pr\u00e9vision du rendement des pommes de terre<\/h2>\n<p>\u00c0 la lumi\u00e8re de ces r\u00e9sultats, les praticiens doivent d&#039;abord choisir la plateforme la plus adapt\u00e9e \u00e0 leurs objectifs. Pour les pr\u00e9visions de rendement \u00e0 l&#039;\u00e9chelle r\u00e9gionale, les donn\u00e9es Sentinel-2 gratuites offrent une couverture fiable avec une r\u00e9solution de 10 m et une p\u00e9riode de revisite de 5 jours.<\/p>\n<p>Pour affiner les estimations locales, des vols de drones programm\u00e9s environ 36 \u00e0 55 jours apr\u00e8s la plantation permettent de saisir la dynamique du couvert v\u00e9g\u00e9tal et d&#039;am\u00e9liorer le calibrage des mod\u00e8les satellitaires. Les capteurs au sol sont surtout utiles pour les v\u00e9rifications ponctuelles et pour calibrer les observations \u00e0 distance, notamment lorsqu&#039;ils combinent des donn\u00e9es spectrales avec des mesures de terrain.<\/p>\n<p>En ce qui concerne les indices de v\u00e9g\u00e9tation, les praticiens devraient privil\u00e9gier le NDVI, le NDRE et le CI <sub>red-edge<\/sub> pour pr\u00e9dire le rendement final, car ceux-ci pr\u00e9sentent syst\u00e9matiquement de fortes corr\u00e9lations.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/fr\/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-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=\"Recommandations pour la pr\u00e9vision du rendement des pommes de terre\" 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>Pour estimer les teneurs en chlorophylle et en azote, la combinaison des indices de la limite rouge avec des indices de v\u00e9g\u00e9tation ajust\u00e9s au sol (comme TCARI\/OSAVI) donne les r\u00e9sultats les plus pr\u00e9cis. Pour l&#039;estimation de la biomasse, l&#039;int\u00e9gration des indices de v\u00e9g\u00e9tation avec la hauteur des plantes ou la texture du sol dans des mod\u00e8les d&#039;apprentissage automatique am\u00e9liore encore la pr\u00e9cision.<\/p>\n<p>En mati\u00e8re de mod\u00e9lisation, les r\u00e9gressions lin\u00e9aires ou non lin\u00e9aires simples utilisant un seul indice sont efficaces lorsque les donn\u00e9es de terrain sont limit\u00e9es. Cependant, lorsque plusieurs indices et des donn\u00e9es auxiliaires (m\u00e9t\u00e9o, sol, gestion) sont disponibles, les m\u00e9thodes d&#039;apprentissage automatique telles que les for\u00eats al\u00e9atoires ou les r\u00e9seaux de neurones offrent des performances sup\u00e9rieures. Il est important de noter que l&#039;acquisition d&#039;images entre 36 et 55 jours apr\u00e8s la plantation est cruciale, car cette p\u00e9riode permet d&#039;obtenir syst\u00e9matiquement la meilleure pr\u00e9cision de pr\u00e9diction.<\/p>\n<h2>Conclusion<\/h2>\n<p>En conclusion, la t\u00e9l\u00e9d\u00e9tection offre un ensemble d&#039;outils rapides, flexibles et pr\u00e9cis pour le suivi de la croissance des pommes de terre et la pr\u00e9vision des rendements en tubercules. En choisissant la plateforme appropri\u00e9e, en s\u00e9lectionnant les indices de v\u00e9g\u00e9tation les plus pertinents, en programmant la collecte des donn\u00e9es entre 36 et 55 jours apr\u00e8s la plantation (JAP) et en appliquant des techniques de mod\u00e9lisation ad\u00e9quates, les chercheurs et les praticiens peuvent am\u00e9liorer significativement les pr\u00e9visions de rendement.<\/p>\n<p>Cette approche permet non seulement de gagner du temps, mais aussi de prendre des d\u00e9cisions de gestion plus judicieuses, ce qui profite en fin de compte aux agriculteurs, aux agronomes et \u00e0 l&#039;ensemble de la fili\u00e8re pomme de terre.<\/p>\n<p><strong>R\u00e9f\u00e9rence<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>et al.<\/i>\u00a0Une revue syst\u00e9matique des indices de v\u00e9g\u00e9tation pour le suivi de la croissance des pommes de terre et la pr\u00e9diction du rendement des tubercules \u00e0 partir de la t\u00e9l\u00e9d\u00e9tection.\u00a0<i>R\u00e9serve de pommes de terre.<\/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>La pomme de terre est l&#039;une des cultures vivri\u00e8res les plus importantes au monde, constituant un aliment de base pour des millions de personnes. Tout d&#039;abord, il est essentiel de comprendre comment poussent les plants de pommes de terre\u2026<\/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_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_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 v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard<\/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\/fr\/blog\/les-indices-de-vegetation-issus-de-la-teledetection-transforment-les-previsions-de-rendement-de-la-pomme-de-terre\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard\" \/>\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\/fr\/blog\/les-indices-de-vegetation-issus-de-la-teledetection-transforment-les-previsions-de-rendement-de-la-pomme-de-terre\/\" \/>\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=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"dementievgeopard\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\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\":\"fr-FR\",\"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\",\"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\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/geopard.tech\\\/blog\\\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@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\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#organization\",\"name\":\"GeoPard Agriculture\",\"alternateName\":\"GeoPard\",\"url\":\"https:\\\/\\\/geopard.tech\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1\",\"contentUrl\":\"https:\\\/\\\/i0.wp.com\\\/geopard.tech\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1\",\"width\":512,\"height\":68,\"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\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCiaPGLAhRPNh-s85dXdC-Sw\",\"https:\\\/\\\/www.g2.com\\\/products\\\/geopard-agriculture-precision-farming-software\\\/reviews\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/geopard.tech\\\/#\\\/schema\\\/person\\\/dd217733c742620adc57befbbcd84a8a\",\"name\":\"dementievgeopard\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@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\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Les indices de v\u00e9g\u00e9tation issus de la t\u00e9l\u00e9d\u00e9tection transforment les pr\u00e9visions de rendement de la pomme de terre - GeoPard","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\/fr\/blog\/les-indices-de-vegetation-issus-de-la-teledetection-transforment-les-previsions-de-rendement-de-la-pomme-de-terre\/","og_locale":"fr_FR","og_type":"article","og_title":"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard","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\/fr\/blog\/les-indices-de-vegetation-issus-de-la-teledetection-transforment-les-previsions-de-rendement-de-la-pomme-de-terre\/","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":{"\u00c9crit par":"dementievgeopard","Dur\u00e9e de lecture estim\u00e9e":"6 minutes"},"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":"fr-FR","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":"Les indices de v\u00e9g\u00e9tation issus de la t\u00e9l\u00e9d\u00e9tection transforment les pr\u00e9visions de rendement de la pomme de terre - GeoPard","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":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/geopard.tech\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@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 - Logiciel d'agriculture de pr\u00e9cision","description":"Logiciel de cartographie pour l'agriculture de pr\u00e9cision","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":"fr-FR"},{"@type":"Organization","@id":"https:\/\/geopard.tech\/#organization","name":"GeoPard Agriculture","alternateName":"GeoPard","url":"https:\/\/geopard.tech\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/geopard.tech\/#\/schema\/logo\/image\/","url":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2026\/07\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1","contentUrl":"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2026\/07\/geopard_logo_transparentbackground.png?fit=512%2C68&ssl=1","width":512,"height":68,"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\/","https:\/\/www.youtube.com\/channel\/UCiaPGLAhRPNh-s85dXdC-Sw","https:\/\/www.g2.com\/products\/geopard-agriculture-precision-farming-software\/reviews"]},{"@type":"Person","@id":"https:\/\/geopard.tech\/#\/schema\/person\/dd217733c742620adc57befbbcd84a8a","name":"l'ours d\u00e9mentiel","image":{"@type":"ImageObject","inLanguage":"fr-FR","@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"}}]}},"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\/fr\/wp-json\/wp\/v2\/posts\/11785","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/users\/210157960"}],"replies":[{"embeddable":true,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/comments?post=11785"}],"version-history":[{"count":0,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/posts\/11785\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/media\/11791"}],"wp:attachment":[{"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/media?parent=11785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/categories?post=11785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geopard.tech\/fr\/wp-json\/wp\/v2\/tags?post=11785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}