Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks.

Abstract

Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

Plant phenotyping relevance

トマト葉画像から病害状態を推定する深層学習手法を開発・評価しており、植物の病徴認識が研究の中心です。

Code and data availability

The paper's phenotyping inputs are public leaf image datasets: the tomato leaf disease images from PlantVillage and the Flavia leaf dataset, both explicitly cited with public download URLs in the Data Availability Statement and Methods. No author code, models, or checkpoints are reported.