UAV-based Systems for Advanced Crop Growth Monitoring with Deep Learning Framework in Complex Agriculture

Abstract

Crop monitoring is paramount to ensure effective and sustainable agricultural practices. These activities provide crucial information about crop health, development, and yield, enabling farmers to make informed decisions and enhance their farming practices. However, deep learning has proven to be a vital tool. It allows the automated analysis of vast agricultural data, delivering precise and timely information for proactive crop management and resource allocation decision-making. Based on an enhanced convolutional neural network model, the proposed framework focuses on detecting three key growth stages in Vicia faba L. cultivation within challenging and intricate environments. The dataset utilized in this study comprises images representing diverse developmental phases of crops collected through Unmanned Aerial Vehicles (UAVs) at an agricultural farm during different periods. Four distinct models within the framework were evaluated based on classification accuracy, mean average precision (mAP), and F1 score. The results indicate that the model with the highest classification accuracy reached 91.6\%, with a commendable mAP of 90.7\%. In contrast, the model with the lowest accuracy achieved a precision of 88.2\%. The empirical validation of the framework in a complex agricultural environment aligns seamlessly with the demands of modern farming operations, demonstrating notable improvements in precision and reliability.

Plant phenotyping relevance

UAV画像からソラマメの生育ステージを推定する深層学習フレームワークを開発し、複数モデルの精度検証も行っており、植物表現型取得手法が中心である。

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