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Author(s):

K. Nithilan | KSJ. Nilesh | MS. Preethish | R. Sindhiya
Keywords:

Plant Disease Detection, Deep Learning, Convolutional Neural Network, Transfer Learning, Image Classification, Leaf Image Analysis, ResNet, Precision Agriculture, Crop Disease Diagnosis, Feature Extraction.
Abstract:
Agriculture Plays A Crucial Role In Global Food Security, And Plant Diseases Represent One Of The Most Significant Threats To Crop Productivity And Quality Worldwide. Traditional Methods Of Disease Identification Rely Heavily On Manual Inspection By Agricultural Experts, Which Is Time-consuming, Costly, And Prone To Human Error. To Overcome These Limitations, This Work Proposes A Deep Learning-based Plant Disease Detection System Capable Of Automatically Identifying And Classifying Diseases From Leaf Images. The Proposed Framework Employs Convolutional Neural Networks (CNN) To Extract Discriminative Features From Plant Leaf Images And Performs Multi-class Disease Classification With High Accuracy. Transfer Learning Techniques Using Pre-trained Models Such As ResNet And VGG Are Incorporated To Improve Generalization And Reduce Training Time. The System Enables Early And Accurate Disease Diagnosis, Allowing Farmers To Take Timely Corrective Action And Minimize Crop Losses. Experimental Results Demonstrate That The Proposed Model Achieves Competitive Accuracy On Benchmark Plant Disease Datasets, Making It A Reliable Tool For Precision Agriculture Applications.