A Study on Various Techniques for Plant Leaf Disease Detection Using Leaf Image

Agriculture plays an important role in the economy of any country and there are a lot of varieties of crops for farmers. The problem or issues occurs when the crops are infected by some disease and the farmers do not know about that disease of plants at the right time. And when the disease is detected, the farmers do not know which disease it is. Therefore, the examination of automatic leaf disease detection in agriculture is a fundamental subject of research as it could display advantages in the observation of vast fields of yields and thus identify manifestations of disease as they occur on plant leaves. The study of plant disease means the study of different patterns visible with the eyes above the plants' leaves. By looking at the different color and texture features of the same plant, now it can be analyzed that which portion of the plant is healthy and which part of the plant has a disease. The process of knowing the disease of the plant occurs in the laboratory. This process takes a lot of time and it is very expensive. For that, the researcher used different types of techniques so that disease will be detected on time and expenses should be reduced. So, this research work attempts to describe the approach suggested by the study articles. Different scholars view the images in terms of Artificial Intelligence, Machine learning and demonstrate their achievements and problems that still exist. To draw some assumptions, our study of the various approaches suggested is also given. Image Acquisition, Image Preprocessing, Image segmentation, Feature Extraction and Statistical Analysis, Classification based on classifier are the key steps for the identification of diseases. This paper provides, along with the available datasets, a survey of the available approaches to solving the problem discussed.