Capsicum Leaf Disease Detection Using Different Learning Techniques

The cultivation of capsicum, an important vegetable crop, can be found in a wide variety of nations around the world. On the other hand, illnesses such as those that affect the leaves can dramatically lower crop production. If these diseases are recognized and treated in a timely manner, it is possible to prevent a loss in yield. Image processing and machine learning are two methods that we propose using in this study to identify diseases that are present on capsicum leaves. The technique that is recommended involves taking images of the leaves and then studying those image dataset. The algorithms that make up machine learning classify these characteristics into separate groupings. The findings of the tests that were conducted are reported and compared in this study in order to demonstrate that the method that was proposed for diagnosing illnesses in capsicum leaf is preferable. On the other hand, the infrastructure that is required to detect crop diseases in a timely manner is inadequate in many regions of the world. Deep learning is responsible for both the rising prevalence of smartphone use and the more recent progress made in computer vision. This essentially means that sick people can now use their phones to acquire a diagnosis of whatever ails them. The application of machine learning may be seen in almost every aspect of contemporary life. In today's modern culture, it can be used for a variety of purposes, such as in the medical profession, in classification, and in identification. In this study, several different learning algorithms, one of which was a deep convolutional neural network, were tested to determine whether or not they had the ability to identify diseases in plant leaves in real time. Farmers typically rely on methods that are simply physical to detect diseases. This is because they do not have sufficient understanding about how leaf infections develop themselves. In most cases, a reduction in productivity is observed alongside the course of the disease. However, because basic infrastructure is insufficient in many parts of the world, it is necessary to develop improved ways of fast identification. It is now possible to diagnose sickness with the use of smartphones thanks to the broad usage of smartphones as well as breakthroughs in computer vision made possible by deep learning.