Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.

Abstract

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.

Plant phenotyping relevance

トマトの病害・害虫を実圃場画像から検出・診断する深層学習手法を体系的にレビューしており、植物の病徴・状態を画像から推定する方法論が中心である。

Code and data availability

This is a review article; it reports no author-generated phenotype datasets, images, code, or models. All datasets mentioned (PlantDoc, PlantVillage, Mendeley tomato leaf dataset) belong to cited prior work, and figure photo sources are generic external image sites, not paper-specific phenotyping assets.

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