Analysis of Tomato Leaf Disease Identification Techniques

India loses thousands of metric tons tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses farmers possess threat the agriculture sector. Understanding how does an algorithm learn classify different types will help scientist engineers built accurate models for detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant However, human benchmarks still not been displayed by any computer vision method. Under conditions, accuracy identification system much lower than expected algorithms. This study performs analysis on features learned studies state-of-the-art results image-based classification methods. The shown through gradient-based visualization In our analysis, most descriptive approach generated attention maps Grad-CAM. Moreover, it also using learning possible achieve comparable deep models. Hence, might show Neural Network achieves supervised learning. But, both genetic semi-supervised hold potential precise systems