Uav for Crop Monitoring System Using Computer Vision

Abstract

Abstract This study focuses on the vital task of detecting Banana Black Sigatoka in banana plants using a cutting-edge method that combines deep learning algorithms with Unmanned Aerial Vehicles (UAVs). The research includes building a detailed dataset that features images of both healthy and infected banana plants. A variety of deep learning algorithms, such as convolutional neural networks and residual networks, are thoroughly tested to select the most effective model for analyzing this dataset. The selected algorithm is then integrated into a UAV-based system for the real-time detection of Black Sigatoka within banana plantations. This proactive strategy allows for the quick detection and localization of affected plants, making it possible to intervene promptly and improve overall crop management. The proposed method marks a significant step forward in using technology for precision agriculture, aiming to enhance the resilience and productivity of banana farming.

Plant phenotyping relevance

バナナ葉の病害状態を画像から推定する深層学習モデルを開発・比較し、UAVシステムへ統合して実地検出する研究であり、植物病害フェノタイプの取得手法が中心です。

Code and data availability

The paper describes a banana Black Sigatoka image dataset (11,399 images) and deep learning model training, but provides no public deposit, repository, or availability statement for the dataset, images, code, or trained models. The only supplementary file is Tables.docx (tables only).

No evidence-backed public reproduction asset is currently recorded.