LAUSR

BACKGROUND The diseases such as Alternaria and pests such as Leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from Leafminer pest at the early and middle stages of their outbreak on tomato leaves. In this study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and Leafminer at the initial stages. The image processing technique, texture, neural networks (NNs), and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and Leafminer pest on this dataset. RESULTS The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate (1-EER), 87.78% in area under the curve (AUC), and 85.23% in 3.26 seconds on the CPU for the segmentation of Alternaria disease and Leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space. CONCLUSIONS This study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets. This article is protected by copyright. All rights reserved.