Citation:

T. Vignesh, E.Srie Vidhya Janani, 2023. "Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network" ESP Journal of Engineering & Technology Advancements  3(1): 07-14.

Abstract:

Finding plant leaves is a crucial step in preventing a major outbreak. The automatic diagnosis of plant disease is an important research area. Similar to humans and other animals, plants also experience the negative effects of sickness. These diseases affect the entire plant, including the leaf, stem, fruit, root, and flower. More often than not, when a plant's sickness is left untreated, the plant bites the ground or can also cause the loss of leaves, blooms, natural products, and so forth. For accurate identification and treatment of plant diseases, these disorders must be properly dedicated. The study of plant infections, their causes, and methods for containing and managing them is known as plant pathology. However, the modern strategy emphasises human inclusion for order and differentiating disease evidence. This strategy is time-consuming and expensive. Programmable disease detection from plant leaf images using a sensitive registration technique may be more valuable than the existing one. In this research, we present a method for identifying symptoms and characterising plant leaf illnesses organically called Bacterial looking development based entirely Radial Basis Function Neural Network (BRBFNN). We employ bacterial looking streamlining (BFO), which also increases the speed and accuracy of the device, to give Radial Basis Function Neural Network (RBFNN) the best possible weight when understanding various illnesses at the plant Leaf's. The suggested method improves recognition of evidence and infection characterisation.

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Keywords:

Neural Network (BRBFNN), Plant Pathology, Infection Characterisation, Image Processing, Analog and digital image processing, Digital Processing Techniques.