Deep learning models with a hierarchical method using RGB images from UAV and proximal sensor data for real-time detection of strawberry plant health

Abstract

The integration of artificial intelligence (AI), machine learning (ML), and precision agriculture has created new opportunities for efficient and sustainable crop monitoring. These technologies enable large-scale analysis of agricultural data to assess plant health, optimize resource usage, and support data-driven decision-making. This work presents a machine-learning-based framework for assessing strawberry plant health using RGB imagery collected from unmanned aerial vehicles (UAVs). Unlike traditional object detection approaches, this study adopts a hierarchical classification strategy using convolutional neural networks, including different ResNet and EfficientNet architectures. Individual plant regions are extracted as blobs through a preprocessing pipeline, and these image tiles are used to train stage-wise binary classifiers that progressively distinguish plant health categories. To enhance reliability, model predictions are validated using field-collected ground-truth data including chlorophyll measurements and visual plant health ratings, as well as real-time deployment scenarios, where predictions are made from live UAV video feeds. Geospatial alignment associates image-based predictions with real-world measurements, enabling comprehensive evaluation of model performance. Experimental results showed that ResNet18 achieved 87.75% accuracy with an F1 score of 0.8524 for healthy plant classification and 93.75% accuracy with an F1 score of 0.6115 for unhealthy plant classification. EfficientNet-B0 demonstrated superior performance for moderately healthy and moderately unhealthy categories, achieving accuracies of 66.83% and 75.65%, with F1 scores of 0.6350 and 0.6070, respectively, highlighting the effectiveness of the hierarchical classification framework. This framework demonstrates the practical potential of RGB-based plant health monitoring integrated with geospatial alignment and field-validated measurements, offering a scalable, efficient solution for precision agriculture applications.

Plant phenotyping relevance

RGB画像と深層学習を用いてイチゴ個体の健康状態を推定する分類フレームワークを開発し、地上測定・目視評価・実運用映像で検証しており、植物表現型取得が中心的です。

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