Paper Title
Machine Learning Approach in Agriculture Analysis: A Case Study to Detect Plant Disease

Abstract
Plant diseases significantly impact crop yields and quality, posing a perennial threat to agricultural productivity. Given that the Indian agriculture sector contributes nearly 18% to the country’s GDP, any decline in crop output could lead to economic setbacks and jeopardize national food security. India currently faces losses ranging from 15-25% in potential crop yield due to weeds, diseases, and insects. The vulnerability of developing countries, heavily reliant on agricultural produce, raises concerns about potential famine and inflation resulting from even minimal losses. Conventional methods for disease detection incur high costs, with visual crop inspection being time-consuming and requiring specialized expertise, often leading to inaccurate disease classification and rendering prophylactic measures ineffective. Accessibility to plant pathologists is also a challenge in some instances. In light of these challenges, there is a pressing need to enhance the efficiency and accessibility of plant disease detection systems. This review explores the integration of Machine Learning (ML) and Deep Learning (DL) techniques in the agricultural sector providing a comprehensive and comparative analysis of various methods. Through a systematic review approach, this paper addresses existing inconsistencies and offers insights into the latest methodologies shaping the future of plant disease detection. Keywords - Plant Leaf Diseases, Deep Learning, Classification, Machine Learning.