Paper Key : IRJ************883
Author: Harsha M S,K R Sumana
Date Published: 02 Aug 2022
Abstract
Mangifera Indica (Mango) also known as The King of Fruits, One of the significant fruit crops grown in numerous nations worldwide is the mango. India is the top mango-producing nation in the world, contributing roughly 40% of the total production. Pests and diseases are thought to be responsible for 30 to 40 percent of the agricultural yield loss. Mango quality and productivity are negatively impacted by mango leaf diseases. Mango leaf disease is challenging to identify with the naked eye without assistance from a professional. To increase the quality and quantity of mango produce, disease control methods must first be taken to identify leaf diseases. Therefore, it's crucial to identify leaf infections as soon as possible. Automating disease identification is particularly advantageous since it lessens the monitoring effort required in large farms. We suggested a new framework for mango leaf disease categorization, using 439 photos from mango growing area in Mysuru, Mandya, India (257 from Mandya, 182 from Mysore). This work uses deep learning models like 6-Layered and VGG-16 that are based on CNN technology to automatically identify and categorize leaf diseases in several mango plant types. A collection of photos of both sick and healthy mango leaves has revealed the presence of four major leaf illnesses, including Mealy Bug, Pests, Cashew Leaf Miner, Low in Potassium, and Iron.