Performance Improvement of Rice Leaf Disease Prediction Model using Enhanced Super-Resolution Generative Adversarial Networks

The objective of this paper is to propose a method to improve the performance of Rice Leaf Disease Prediction Deep Learning Model. The primary goal is to empower farmers with precise information about crop disease, enabling them to judiciously apply pesticides suitably to their crop's needs. The method proposed here uses image enhancement using Enhanced Super Resolution Generative Adversarial Network. Experiment was carried out using Rice Leaf Disease dataset which includes 16,000 rice leaf images categorized into four groups. Among these groups, three contain 4,000 images each, showcasing three different diseases and another 4000 images of healthy leaves. The best accuracy of the model came out to be 99.68. Also all individual diseases are identified very well and other model evaluation aspects also giving good results. Average precision obtained is 99.67, average recall value=99.68 and average f1-score came out to be 99.67. Conclusion: The performance results obtained is the highest what has been achieved of all the models that have been developed till now for this task.