TomConv: An Improved CNN Model for Diagnosis of Diseases in Tomato Plant Leaves

• Published in: Procedia computer science Vol. 218; pp. 1825 - 1833
• Main Authors: Baser, Preeti, Saini, Jatinderkumar R., Kotecha, Ketan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: Computer Vision; Convolutional Neural Network (CNN); Dense Layer; Fully Connected Network (FCN); Max Pooling; Rectified Linear Unit (ReLU); Softmax; Tomato; Rectified Linear Unit (ReLU); Dense Layer; Max Pooling; Softmax; Computer Vision; Fully Connected Network (FCN); Tomato; Convolutional Neural Network (CNN)
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.01.160

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models.