Harnessing Deep Learning for Accurate Potato Leaf Disease Identification

With a long history of cultivation spanning several decades, potatoes are one of the mainstay crops in agriculture. Despite its continued appeal, the spread of illnesses poses serious challenges to potato production, increasing expenses and upsetting farmers. To boost potato yields and usher in a new era of digitalized agriculture techniques, it is becoming more and more clear that quick and automated ease detection processes are desperately needed. Utilizing state-of-the-art machine learning technology to analyse leaf photos and diagnose potato illnesses is the main goal of this research. The suggested remedy manifests as an advanced automated system that seamlessly combines machine learning and image processing technologies to achieve the results. Training accuracy of $99.48 \%$ and a Validation accuracy of $99.22 \%$ were obtained using Convolution Neural Networks (CNN).