Detection of Potato Leaf Disease using CNN for High-Accuracy Diagnosis

Detection of Potato Leaf Disease using CNN for High-Accuracy Diagnosis

Shivam Tripathi

Department of Computer Science and Engineering Sharda University Greater Noida, India

2022005275.shivam@ug.sharda.ac.in

Priyanshu Dimri

Department of Computer Science and Engineering Sharda University Greater Noida, India

2022005818.priyanshu@ug.sharda.ac.in

Sandeep Kumar

Department of Computer Science and Engineering Sharda University Greater Noida, India

sandeep.kumar16@sharda.ac.in

Abstract: Crop production rates are a major problem for India, an agricultural nation. Low output makes the crops expensive and causes hunger for people who cannot even buy basic necessities like potatoes. Deep learning technologies can make farmers' lives easier and more productive by increasing agricultural yield and reducing crop disease infections. A subfield of artificial intelligence called deep neural networks helps farmers detect plant diseases early on, giving them enough time to take action before it's too late. Convolutional Neural Networks (CNN) are better at detecting leaf illnesses, according to this paper's evaluation of multiple studies. Additionally, CNNs have the highest accuracy in identifying diseases and are able to identify even minute patterns that are invisible to the naked eye.

Keywords— Crop Production, Deep Learning, Plant Disease Detection, Convolutional Neural Networks (CNN), Agricultural Yield, Artificial Intelligence in Agriculture.