  The agriculture sector is essential for every country because it provides a
basic income to a large number of people and food as well, which is a
fundamental requirement to survive on this planet. We see as time passes,
significant changes come in the present era, which begins with Green
Revolution. Due to improper knowledge of plant diseases, farmers use
fertilizers in excess, which ultimately degrade the quality of food. Earlier
farmers use experts to determine the type of plant disease, which was expensive
and time-consuming. In today time, Image processing is used to recognize and
catalog plant diseases using the lesion region of plant leaf, and there are
different modus-operandi for plant disease scent from leaf using Neural
Networks (NN), Support Vector Machine (SVM), and others. In this paper, we
improving the architecture of the Neural Networking by working on ten different
types of training algorithms and the proper choice of neurons in the concealed
layer. Our proposed approach gives 98.30% accuracy on general plant leaf
disease and 100% accuracy on specific plant leaf disease based on Bayesian
regularization, automation of cluster and without over-fitting on considered
plant diseases over various other implemented methods.
