Lightweight Isotropic Convolutional Neural Network for Plant Disease Identification

Abstract

In today’s world, agricultural products are becoming increasingly scarce globally due to a variety of factors, and the early and accurate automatic identification of plant diseases can help ensure the stability and sustainability of agricultural production, improve the quality and safety of agricultural products, and help promote agricultural modernization and sustainable development. For this purpose, a lightweight deep isotropic convolutional neural network model, FoldNet, is designed for plant disease identification in this study. The model improves the architecture of residual neural networks by first folding the chain of the same blocks and then connecting these blocks with jump connections of different distances. Such a design allows the neural network to explore a larger receptive domain, enhancing its multiscale representation capability, increasing the direct propagation of information throughout the network, and improving the performance of the neural network. The FoldNet model achieved a recognition accuracy of 99.84% on the laboratory dataset PlantVillage using only 685k parameters and a recognition accuracy of 90.49% on the realistic scene dataset FGVC8 using only 516k parameters, which is competitive with other state-of-the-art models. In addition, as far as we know, our model is the first model that has fewer than 1M parameters while achieving state-of-the-art accuracy in plant disease identification. This proposal facilitates precision agriculture applications on mobile, low-end terminals.

Plant phenotyping relevance

植物画像から病害状態を識別する深層学習モデルの開発と複数データセットでの性能評価が中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper's plant-phenotyping inputs are two public plant disease image datasets (PlantVillage and FGVC8) explicitly declared as openly available in the Data Availability Statement. No author analysis code, trained model checkpoints, or paper-specific supplements with code are reported.