NeuroQuantology | November 2022 | Volume 20 | Issue 16 | Page 2102-2106 | doi: 10.48047/NQ.2022.20.16.NQ880300 Sandeep U. Kadam et al / Machine Learning Methode for Automatic Potato Disease Detection eISSN 1303-5150 www.neuroquantology.com 2102 Machine Learning Methode for Automatic Potato Disease Detection Sandeep U. Kadam 1* , Vaishali M. Dhede 2 , Vajid N Khan 3 , Atul Raj 4 , Dattatray G. Takale 5 Abstract The potato is a major crop in India. Potato producing fields in Bangladesh have increased over the last several decades. However, a variety of ailments are reducing potato yields and raising the prices that farmers must pay to continue growing potatoes. However, a variety of potato-specific diseases are reducing yields and increasing production costs. Farmers are having a harder time due to an automated and quick disease detection approach meant to increase potato yield and digitalize the system. Our main goal is to employ state-of-the-art machine learning technology to examine pictures of potato plant leaves in order to identify illnesses. The goal of this study is to automate the process of recognising and categorising illnesses affecting potato leaves by means of image processing and machine learning. The most reliable approach for detecting and researching these conditions is image processing. More than 2034 images of sick potatoes and potato leaves were extracted from a public database of plant towns for this analysis. To further aid in the differentiation between unhealthy and healthy leaves, a small number of previously developed models were also used. Our research has shown that, when it comes to diagnosing potato diseases, machine learning is head and shoulders above any other technique. Key Words: Leaf disease classification, deep learning, vgg16, resnet50, google-net, potato plant DOI Number: 10.48047/NQ.2022.20.16.NQ880300 NeuroQuantology 2022; 20(16):2102-2106 Introduction Of Cryptography Although there are many other types of jobs that may be done, agriculture is by far the most common. India is no exception to this trend, since its economy is also heavily reliant on the agricultural sector. In India, the most versatile crop is the potato, which produces around 28.9 percent of the country's overall agricultural crop production. Potatoes are the world's fourth most widely grown staple crop, behind corn, wheat, and rice. With an annual output of 48.5 million metric tonnes [1], India is the world's second-largest producer of potatoes. Uttar Pradesh, according to the Agricultural and Processed Food Products Export Development Authority (APEDA), is the most productive state in India in terms of potato production, accounting for almost 30.33 percent of the country's total potato output. Potato starch, often called farina, is used to scale cotton and worsted in the textile business. Potassium, phosphorus, vitamin C, and vitamin B6 are all abundant in potatoes. It lowers total cholesterol levels in the blood, which aids in the treatment of diseases including high blood pressure, heart disease, and cancer. Diseases cause damage both to plants and the soil that is used for agriculture. These disorders are caused by microorganisms, genetic problems, and infectious agents such as bacteria, fungus, and viruses, among other infectious agents. Fungi and bacteria are often the culprits behind diseases that affect potato leaves. Bacteria are to fault for soft rot and common scab, but fungi are responsible for late blight and early blight [2]. We are motivated to develop an automated technique for identifying and diagnosing illnesses on such important plants Corresponding author: Sandeep U. Kadam Address: 1 Associate Professor, Department of Computer Engineering, Anantrao Pawar College of Engineering and Research, SPPU, Pune, 2 Associate Professor, Department of Electronic and Telecommunication, Jaihind College of Engineering SPPU, Pune, 3 Assistant Professor, Department of Computer Engineering, Dhole Patil College of Engineering, Wagholi, Pune, 4 Lectural, Department of Computer engineering, Government Polytechnic, Sikandra, Kanpur, Dehat, 5 Assitant Professor, Department of Computer Engineering, Vishwakarma Institute of information Technology, SPPU, Pune E-mail: sandeep.kadam@abmspcoerpune.org