ORPN Algorithm to Diagnosis and Detect Plant Diseases Based on Image Segmentation Using Machine Learning Techniques

EasyChair Preprint 9760

Abstract

The Plant disease modernization in agricultural land is the main concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Indian economy is extremely dependent of farming productivity. Therefore, in field of cultivation, detection of disease in floras plays an important role. Also, the increased use of expertise today has increased the efficacy and accuracy of noticing diseases in plants and animals. The thought of classification in machine learning techniques deals with the problem of identifying to which set of categories a new population belongs. Our attention is to illuminate the facts about the diseases and how to perceive them promptly with artificial intelligence. It proposes to deliberate the use of Ai techniques to detect diseases in plants robotically. In this research article, the O-RPN(Optimized Region Proposal Network) is utilized to identify and localize the leaves in complex surroundings. O-RPN Algorithm comprises the feature of indications through Chan–Vese (CV) techniques. The CV algorithm based on region shows promising results for segmenting images free of noise and weak edge. Furthermore, different data sets related to plant diseases are compared with CNN and SVM.

Keyphrases: CNN, RPN, SVM, feature extraction, image segmentation

@booklet{EasyChair:9760,
  author    = {Rajesh Kanna Rajendran and Mohana Priya Thiruvengadam},
  title     = {ORPN Algorithm to Diagnosis and Detect Plant Diseases Based on Image Segmentation Using Machine Learning Techniques},
  howpublished = {EasyChair Preprint 9760},
  year      = {EasyChair, 2023}}