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Article Dans Une Revue Computers and Electronics in Agriculture Année : 2022

Crop stem detection and tracking for precision hoeing using deep learning

Résumé

Developing alternatives to the chemical weeding process usually carried out in vegetable crop farming is necessary in order to reach a more sustainable agriculture. However, a precise mechanical weeding requires specific sensors and advanced computer vision algorithms to process crop and weed discrimination in real-time.In this paper we propose an algorithm able to detect, locate, and track the stem position of crops in images which is suitable for precision actions in vegetable fields such as mechanical hoeing within crop rows. The algorithm is twofold: (i) a deep neural network for object detection is first used to detect crop stems in individual RGB images and then (ii) an aggregation algorithm further refines the detections taking advantage of the temporal redundancy in consecutive frames.We evaluated the pipeline on images of maize and bean crops at an early stage of development, acquired in field conditions with a camera embedded in an experimental mechanical weeding system. We reported F1-scores of respectively 94.74% and 93.82% with a location accuracy around 0.7 cm when compared with human annotation. Moreover, this pipeline can operate in real-time on an embedded computer consuming as little power as 30 W.
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Dates et versions

hal-03722088 , version 1 (08-01-2024)

Licence

Paternité - Pas d'utilisation commerciale

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Louis Lac, Jean-Pierre da Costa, Marc Donias, Barna Keresztes, Alain Bardet. Crop stem detection and tracking for precision hoeing using deep learning. Computers and Electronics in Agriculture, 2022, 192, pp.106606. ⟨10.1016/j.compag.2021.106606⟩. ⟨hal-03722088⟩
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