Advanced Machine Learning-Based Capsicum Leaf Disease Prediction System

Machine learning with convolutional neural networks has shown great success in classifying various plant diseases. This proposal provides a summary and analysis of current methods for the detection of capsicum leaf diseases. Several simulation techniques for neurons and layers were applied in this investigation, which used a CNN equipped with a collection of images of capsicum plants. The decline in quantity and quality of agricultural products is caused by foliar diseases, in addition to financial losses. Good plant health and reliable disease detection data can aid in disease control. Agricultural production will increase owing to this strategy. For this reason, insect population management is crucial to managing problems with capsicum plants. Like viruses or wilt, diseases related to capsicum plants will completely destroy the garden. In the event of a capsicum plant issue, it is best to get rid of the diseased plant as soon as possible to prevent it from spreading to other plants in the garden. Since convolutional networks may learn features after being trained on bigger samples, there is no need to worry about image quality. An average accuracy of 98.34% may be achieved for identifying capsicum diseases using real-time data collected from 5,000 leaf images.