Physiological Disorder Diagnosis of Plant Leaves Based on Full-Spectrum Hyperspectral Images with Convolutional Neural Network

Abstract

The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted with the development of convergence technology. This study analyzes physiological disorders in plant leaves using hyperspectral images and deep learning algorithms. Data on seven classes for various physiological disorders, including normal, prediction, and the appearance of symptom, were obtained for strawberries subjected to artificial treatment. The acquired hyperspectral images were used as input for a convolutional neural network algorithm without spectroscopic preprocessing. To determine the optimal model, several hyperparameter tuning and optimizer selection processes were performed. The Adam optimizer exhibited the best performance with an F1 score of ≥0.95. Moreover, the RMSProp optimizer exhibited slightly similar performance, confirming the potential for performance improvement. Thus, the novel possibility of utilizing hyperspectral images and deep learning algorithms for nondestructive and accurate analysis of the physiological disorders of plants was shown.

Plant phenotyping relevance

植物葉の生理障害状態をハイパースペクトル画像とCNNで非破壊推定し、モデルの最適化・性能評価まで行うため、フェノタイピング手法が中心です。

Code and data availability

The supplied blocks describe a custom hyperspectral imaging system, strawberry seedling experiments, and CNN analysis, but contain no data availability statement, public dataset link, or author code/model deposit. The only URL besides MDPI/DOI links is a generic cs231n tutorial reference, which is not a paper-specific.

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