Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks

Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks

Ganesh Bahadur Singh (National Institute of Technology, Jalandhar, India), Rajneesh Rani (National Institute of Technology, Jalandhar, India), Nonita Sharma (National Institute of Technology, Jalandhar, India) and Deepti Kakkar (National Institute of Technology, Jalandhar, India)
DOI: 10.4018/IJAEIS.20211001.oa3
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Crop disease is a major issue now days; as it drastically reduces food production rate. Tomato is cultivated in major part of the world. The most common diseases that affect tomato crops are bacterial spot, early blight, septoria leaf spot, late blight, leaf mold, target spot, etc. In order to increase the production rate of tomato, early identification of diseases is highly required. The existing work contains very less accurate system for identification of tomato crop diseases. The goal of our work is to propose cost effective and efficient deep learning model inspired from Alexnet for identification of tomato crop diseases. To validate the performance of proposed model, experiments have also been done on standard pretrained models. The plantVillage dataset is used for the same, which contains 18,160 images of diseased and non-diseased tomato leaf. The disease identification accuracy of proposed model is compared with standard pretrained models and found that proposed model gave more promising results for tomato crop diseases identification.
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1. Introduction

The crop of Tomato is mostly cultivated in a wide area of the globe as it contains three major antioxidants vitamin E, beta-carotene, and vitamin C. In India, the area of cultivation spans approximately around 3,50,000 hectares and became the third-largest producer in the globe (Tm, Prajwala, et al., 2018; Ireri, David, et al., 2019). Tomato leaf diseases cause a major loss in quality and production rates. The diseases in tomato crops are mainly in leaves, stems, and roots. Commonly, the diseases in crops are due to fungi, viruses, and bacteria. Some common diseases that affect tomato crops are early blight, septoria leaf spot, two-spotted spider mite, target spot, bacterial spot, mosaic virus, late blight, curl virus, etc. (Durmus, Halil, et al., 2017). It is very difficult for a human to recognize an accurate class of diseases. Incorrect prediction of diseases causes inaccurate usage of pesticides, which causes loss of quality and production of food. Hence, the recognition of diseases plays a major role in the field of agriculture.

There are various techniques available for crop disease recognition. One of the methods is recognition by the farmer under the supervision of an agricultural expert, which is a time-consuming and expensive methodology (Kamilaris, Andreas, et al., 2018). With in time various technologies like machine learning, computer vision and artificial intelligence has been used for crop disease recognition. Machine learning-based recognition algorithms involve major two steps that are feature extraction and classification (Agarwal, Mohit, et al., 2020). The feature from an image is extracted using an appropriate feature extractor. In classification problems mostly supervised learning classification algorithm is used. Machine learning techniques are applied in various area of the agricultural field like, classification of guava, jamun, tomato, mango, grapes, and apple plants using random forest and support vector machine (SVM) algorithms through its leaf images (Kour Vippon Preet et al., 2019), potato crop diseases identification using multiclass support vector machine (Islam, Monzurul et al., 2017), grape leaf diseases recognition using k-nearest neighbor, support vector machine, and random forest (Krithika N. et al. 2017; Sandika Biswas et al., 2016; Padol, Pranjali, et al. 2016), and recognition of wheat leaf diseases using support vector machine (Nema et al. 2018).

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