Mixed model for counting banana plant leaves using aerial drone images for plant health management

Abstract

The banana is a crucial crop in tropical regions, facing challenges from diseases such as black Sigatoka, which affect its production and quality due to defoliation. This research proposes the use of drones equipped with high-resolution RGB cameras to capture images of banana plantations, employing a hybrid deep learning model that combines detection and semantic segmentation to accurately identify and count banana leaves. Additionally, the metadata from the images provided the geographical coordinates of each plant, exported in shapefiles compatible with Geographic Information Systems (GIS). The results show high accuracy in detection (98.5%) and leaf counting (93.45%), surpassing previous, more costly methods. This facilitates the identification of areas affected by diseases, evidenced in the detection of potential black Sigatoka outbreaks. The ability to make informed decisions based on this data improves agricultural management, promoting sustainable practices and optimizing crop quality and productivity.

Plant phenotyping relevance

ドローン画像と深層学習・セマンティックセグメンテーションによりバナナ葉を検出・計数する手法が中心で、葉数という植物形質を定量化しているため。

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