A lightweight tomato leaf disease identification method based on shared‐twin neural networks

Abstract

AbstractAutomatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small.