Agriculture Plants Disease Detection and Preventive Measures

Abstract

Agriculture serves as a source of energy and a solution to the problem of global warming, in addition to feeding an ever-increasing population. Plant diseases are particularly important because they can have a negative impact on the quality and quantity of crops grown in agriculture. Plant disease identification is critical early on in order to cure and control the illness. The naked eye approach is commonly used to diagnose illnesses. Experts with the ability to identify changes in leaf color are involved in this procedure. This procedure involves a great deal of labor, takes a long time, and is impractical for vast fields. Often, multiple professionals would diagnose the same ailment as a different one. This technology is costly since it necessitates expert monitoring on a continuous basis. Plant diseases can raise the cost of agricultural production and, if not treated early enough, can lead to a producer's utter financial ruin. Producers must monitor their crops and recognize early symptoms of a plant disease in order to limit the spread of the illness at a cheap cost and save the majority of the crop. Hiring expert agriculturists may be prohibitively expensive, particularly in remote and isolated geographic areas. A deep learning algorithm in an image can provide an alternate solution for plant monitoring, and such an approach can be controlled by a professional to provide lower-cost services. It contains features extraction and classification, as well as an image classification strategy that uses a neural network algorithm to forecast various diseases in corn leaf. Also, based on severity analysis and data, extend the approach to prescribe fertilizers.