Automated Detection of Rice Bakanae Disease via Drone Imagery.

Abstract

This paper proposes a system for the forecasting and automated inspection of rice Bakanae disease (RBD) infection rates via drone imagery. The proposed system synthesizes camera calibrations and area calculations in the optimal data domain to detect infected bunches and classify infected rice culm numbers. Optimal heights and angles for identification were examined via linear discriminant analysis and gradient magnitude by targeting the morphological features of RBD in drone imagery. Camera calibration and area calculation enabled distortion correction and simultaneous calculation of image area using a perspective transform matrix. For infection detection, a two-step configuration was used to recognize the infected culms through deep learning classifiers. The YOLOv3 and RestNETV2 101 models were used for detection of infected bunches and classification of the infected culm numbers, respectively. Accordingly, 3 m drone height and 0° angle to the ground were found to be optimal, yielding an infected bunches detection rate with a mean average precision of 90.49. The classification of number of infected culms in the infected bunch matched with an 80.36% accuracy. The RBD detection system that we propose can be used to minimize confusion and inefficiency during rice field inspection.

Plant phenotyping relevance

ドローン画像からイネの感染株・感染茎数を抽出し、カメラ校正、撮影条件最適化、深層学習検出・分類精度を評価する手法研究であり、植物病害状態の取得が中心である。

Code and data availability

The paper's drone imagery dataset (2912 cropped RBD/normal rice images, labeled bounding boxes, culm counts) and trained YOLOv3/ResNetV2 models are not publicly deposited; the Data Availability Statement says they are available only from the corresponding author on reasonable request. No authors' public code or dataURL

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