Recognition of Tomato Leaf Diseases Based on DIMPCNET

Abstract

The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we propose a novel classification network for tomato leaf disease, the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET). To begin, we collected a total of 1256 original images of 5 tomato leaf diseases and expanded them to 8190 using data enhancement techniques. Next, an improved bilateral filtering and threshold function (IBFTF) algorithm is designed to effectively remove noise. Then, the Dense Inception convolutional neural network module (DI) was designed to alleviate the problem of large intra-class differences and small inter-class differences. Then, a parallel convolutional block attention module (PCBAM) was added to MobileNet-V2 to reduce the impact of complex backgrounds. Finally, the experimental results show that the recognition accuracy and F1-score obtained by DIMPCNET are 94.44% and 0.9475. The loss is approximately 0.28%. This method is the most advanced and provides a new idea for the identification of crop diseases, such as tomatoes, and the development of smart agriculture.

Maize seed appearance quality assessment based on improved Inception-ResNet

“…Compared with traditional machine learning techniques, convolutional neural networks are more generalizable, faster to train, and can obtain significant information directly from images, which eliminates the tedious steps of manually extracting image features used in traditional methods. In applications for agriculture, convolutional neural networks are often used in areas such as the classification of crop pests and diseases ( Wu et al, 2019 ; Peng et al, 2019 ; Tiwari et al., 2021 ; Liu et al., 2022 ; Liu et al., 2022 ), agricultural product species identification ( Ajit et al., 2020 ; Gao et al., 2020 ; Chen et al, 2021 ; Laabassi et al., 2021 ; Sj et al.,2021 ), yield estimation ( Zhang et al., 2020 ; Tan et al, 2019 ; Alexandros et al, 2023 ; Kavita et al., 2023 ), and crop quality grading ( Anikó and Miklós, 2022 ; Liu et al., 2022 ; Li et al, 2022 ; Wang Z. et al., 2022 ; Peng et al, 2023 ), in which they greatly promote the development of agricultural intelligence. Along with the arrival of the era of big data, the amount of image information increases exponentially, resulting in an increase in the amount of computation and training difficulty in the training process.…”

Research on image recognition of tomato leaf diseases based on improved AlexNet model

“…Peng et al (2023) proposed the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET) for identifying tomato leaf diseases. The results showed that the recognition accuracy and F1 score of DIMPCNET were 94.44 % and 0.9475, respectively, with a loss of approximately 0.28 % [ 22 ]. Jiangtao et al (2022) proposed an improved SE-YOLOv5 network model for identifying tomato virus diseases, with an accuracy of 91.07 % and an average accuracy (mAP (@ 0.5)) of 94.10 % [ 23 ].…”