No abstract

In this varying environment, a correct and appropriate disease diagnosis including early preclusion has never been more significant. Our study on disease identification of groundnut originated by Groundnut Bud Necrosis Virus will cover the way to the effective use of image processing approach in agriculture. The difficulty of capable plant disease protection is very much linked to the problems of sustainable agriculture and climate change. Due to the fast advancement of Artificial Intelligence, the work in this paper is primarily focused on applying Pattern Recognition based techniques. The purpose is to determine the grade of disease to control by developing a model for the selection of bud blight disease caused by GBNV in tomatoes. The images are classified according to the grade of the disease. Different methods have been applied to make a proper diagnosis by bringing clarity in the diagnostic results. Linear Vector Quantization works well than, Radial Basis Function, Back Propagation Neural Network and Support Vector Machine.

In the realm of medicine, value, resource use and final care are determined by good technological advancement. However, there are crucial components that must be present for a disease to be diagnosed. The monitoring of illness progression traditionally relies primarily on a subjective human judgment and is neither precise nor timely. One important aspect that utilizes data at various disease progression phases is to maintain routine disease surveillance. The Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images is provided in this paper as an automatic brain cancer diagnosis and grade classification method. It is highly helpful to have accurate information about the disease in order to classify it and make decisions. The suggested brain tumor diagnosis system can diagnose the condition and provide a reliable foundation for appropriate regulation, as opposed to manual approaches. Finally, the evaluated outcomes of the suggested model investigate numerous Magnetic Resonance Images of healthy and disease and demonstrate that, the proposed method has the highest accuracy.