HortNet417v1—A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress

Abstract

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.

Plant phenotyping relevance

植物の水ストレス状態を画像から自動検出・分類する深層学習手法を開発し、複数データセットで性能評価しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's peach water-stress image dataset (25,000 smartphone images), trained HortNet417v1 model, and analysis code are not publicly deposited. The Data Availability Statement explicitly restricts access: data are available only upon request, subject to NARO review and signed agreements, so no public paper-specific,

No evidence-backed public reproduction asset is currently recorded.