Survey on Various Methods and Algorithms used for Plant Pest and Diseases

With the advancement of technology, there is a greater demand for food to feed more than seven billion people. Skilled workers are hired in traditional farming techniques to manually scout the area and detect the presence of infection in the soil through eye examination. Manual disease diagnosis on cultivated land is time-consuming and labor-intensive. The tedious process might be prone to errors at times. Detecting diseases on cultivated land has become much easier because to new deep learning, machine learning and image processing techniques. This paper looks at a numerous study of pest and plant diseases that have been conducted on tomato crop. The major goal of this study effort is to focus on spinach disorders and find that deep learning beats machine learning, based on survey on tomato crop. In order to construct an automated spinach disease detection system, identified a few hurdles as well as research objectives.