Détection automatique de plantes au sein d’images aériennes de champs par apprentissage non supervisé et approche multi-agents
Revue Ouverte d'Intelligence Artificielle, Volume 2 (2021) no. 1, pp. 123-156.

Cet article a pour objet la détection de plantes à partir d’images aériennes capturées par drones. Une méthode efficace de détection bénéficierait autant aux agriculteurs qui souhaitent connaître l’état de leurs cultures et en prédire le rendement qu’aux agronomes pour faciliter l’acquisition de données expérimentales et en réduire le coût.

Nous proposons une méthode en deux étapes avec, d’abord, un apprentissage non supervisé, puis la mise en œuvre d’un système multi-agent faiblement paramétré. Dans un premier temps, un clustering estime la position des rangs et des plantes en s’appuyant sur des contraintes géométriques connues a priori, à savoir que les rangs de cultures sont globalement équidistants entre eux et qu’il en est de même pour les plantes au sein d’un rang. Dans un second temps, le système multi-agent raffine l’estimation fournie par l’étape de clustering et isole chaque plante.

Nous démontrons les performances de la méthode sur des tâches de comptage de plants de tournesols. Les résultats obtenus sont comparables à ceux de l’état de l’art sur les problèmes faciles et nettement supérieurs sur les problèmes difficiles. Nous présentons ensuite des résultats sur l’identification de gradients de densité dans un champ en fonction de la répartition des plantes qui ont levées.

Une contribution importante de ce travail concerne également la mise au point d’un outil de génération de champs virtuels avec le moteur de jeu Unity. Il devient ainsi possible de générer facilement des jeux de données réalistes correspondant à des situations diverses ce qui permet de contourner la difficulté d’obtenir des jeux de données étiquetés.

This paper focuses on the detection of plants from aerial images captured by drones. An efficient detection method would benefit both farmers who wish to know the state of their crops and predict their yield and agronomists to facilitate the acquisition of experimental data and reduce the cost.

We propose a two-step method with, first, an unsupervised learning, then the implementation of a weakly parameterized multi-agent system. In the first step, a clustering system estimates the position of rows and plants based on a priori known geometric constraints, namely that crop rows are globally equidistant from each other and that the same is true for plants within a row. In a second step, the multi-agent system refines the estimation provided by the clustering step and isolates each plant.

We demonstrate the performance of the method on sunflower plant counting tasks. The results obtained are comparable to those of the state of the art on easy problems and significantly better on hard problems. We then present results on the identification of density gradients in a field based on the distribution of emerged plants.

An important contribution of this work also concerns the development of a tool for generating virtual fields with the game engine Unity. It is thus possible to easily generate realistic datasets corresponding to various situations, thus overcoming the difficulty of obtaining labelled datasets.

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DOI : 10.5802/roia.12
Mots clés : Apprentissage non supervisé, système multi-agents, analyse d’images à partir de drones.
Eliott Jacopin 1 ; Antoine Cornuéjols 1 ; Christine Martin 1 ; Farzaneh Kazemipour 2 ; Christophe Sausse 2

1 UMR MIA-Paris, AgroParisTech, INRAE, Université Paris-Saclay, 75005 Paris, France
2 Terres Inovia, Thiverval-Grignon, France
Licence : CC-BY 4.0
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Eliott Jacopin; Antoine Cornuéjols; Christine Martin; Farzaneh Kazemipour; Christophe Sausse. Détection automatique de plantes au sein d’images aériennes de champs par apprentissage non supervisé et approche multi-agents. Revue Ouverte d'Intelligence Artificielle, Volume 2 (2021) no. 1, pp. 123-156. doi : 10.5802/roia.12. https://roia.centre-mersenne.org/articles/10.5802/roia.12/

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