A Novel Approach for Tomato Diseases Classification Based on Deep Convolutional Neural Networks

Neural networks have achieved a significant result in every arena of information technology, where it has been used to solve any major problem. As a consequence, upward inclination of development has been observed in those industries. Nowadays, because of using NN, the challenges became easier than before to deal with. The agriculture industry is one of the important and largest industries, where technology can make a major contribution by solving their certain problems. Implementation of artificial intelligence can make this industry more successful and faster growing. Since the very beginning, plant diseases are one of the major factors behind low-quality products. So, through identifying those diseases earlier, we can make a great contribution to this agroindustry. Therefore, in this work, a definite detection of tomato diseases has been presented. Several existing and proposed method of identifying the disease through analyzing tomato leaves has been discussed. We have proposed a 15-layered Deep Convolutional Neural Network. Basically, this research will state a basic approach for tomato disease classification. This will be able to classify five different tomato diseases, and the proposed model has achieved fairly high accuracy with low cross-entropy rate. Several simulation results have been measured and discussed.