Identification of Potato Plant Leaf Disease Using Different Artificial Intelligence (AI) Techniques: A Review


  • Sumit Anand Student, Department of Computer Science and Engineering, Lakshmi Narain College of Technology & Science (LNCTS), Bhopal, Madhya Pradesh, India
  • Bhawana Pillai Associate Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology & Science (LNCTS), Bhopal, Madhya Pradesh, India


Computer Vision (CV), Artificial Intelligence (AI), Machine Learning (ML), Potato Plant Disease, Deep learning (DL), Neural Network


One of the nation's most vital industries is agriculture. Employment possibilities are abundant in emerging nations like India. The livelihoods of nearly 70% of the global population depend on agriculture. In this study, we have evaluatednumerous articles that have been released on potato plant leaf disease (PLD)diagnosis and classification utilising machine learning (ML)or Deep Learning (DL)methods. There are several causes of leaf diseases in plants, including bacteria, viruses, fungus, and more. Whether you are a farmer or a scientist, knowing how to spot sick leaves can be a daunting challenge.Farmers spraying their crops with pesticides pose a risk to human health and a financial burden on society. Specialists employ Potato Plant Disease Detection (PDD)methods to help in the fight against and early identification of the disease. This discovery will be a valuable tool for scientists trying to determine whether illnesses affect potato plant leaves by using picture segmentation, ML, DL, and neural network (NN)methods. This document presents an extensive literature review on the diverse diseases affecting potato plant leaves, categorized based on several prominent factors.


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