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Jute Is An Economically Significant Natural Fiber Crop Widely Cultivated In Many Parts Of The World, Particularly In South Asia. However, Its Productivity And Fiber Quality Are Often Threatened By Various Foliar Diseases, Including Stem Rot, Anthracnose, And Leaf Blight, Which Can Cause Severe Yield Losses If Not Detected At An Early Stage. Traditional Methods Of Disease Identification, Which Rely On Manual Field Inspection, Are Labor-intensive, Time-consuming, And Prone To Human Error. To Address These Challenges, This Study Proposes A Deep Learning–based Automated System For Jute Leaf Disease Detection Using The ResNet50 Architecture. ResNet50, A Powerful Convolutional Neural Network (CNN), Is Employed To Extract High-level Discriminative Features From Leaf Images, Enabling Robust Classification Of Healthy And Diseased Leaves. The Model Is Trained And Validated On A Dataset Of Jute Leaf Images, Incorporating Preprocessing And Data Augmentation Techniques To Improve Generalization. Experimental Results Demonstrate That The Proposed Approach Achieves High Accuracy, Precision, And Recall, Making It Suitable For Real-time Deployment In Precision Agriculture. By Enabling Early Detection And Accurate Classification Of Jute Leaf Diseases, This System Can Assist Farmers In Taking Timely Remedial Actions, Thereby Enhancing Crop Yield, Reducing Economic Losses, And Promoting Sustainable Agricultural Practices.