Enhancing Crop Health Monitoring: Deep Learning Approach for Cucumber Leaf Disease Classification
| Vol-07 | Issue-01 | January-2020 | Published Online: 05 January 2020 PDF | ||
| Author(s) | ||
| Rupali Kasar 1; Dr. Garima Shukla 2 | ||
|
1Lecturer, Government Residential Women’s Polytechnic, Department of Computer Science and Engineering, Latur, Maharashtra 2Associate Professor, IIMT Engineering College, Department of Computer Science and Engineering, Meerut, Uttar Pradesh, India |
||
| Abstract | ||
| Cucumber is one of the most widely grown vegetable crops, and its yield is significantly affected by various leaf diseases. Early disease identification is critical for improving crop output and supporting sustainable agricultural practices. This paper proposes a deep learning approach for automated classification of cucumber leaf diseases from image data. The method uses CNNs for feature extraction and classification of healthy and diseased leaves. Image preprocessing and data augmentation were applied to boost model performance. The model was trained and tested on a cucumber leaf image dataset captured under varying environmental conditions. Experimental results show that the proposed approach achieves high classification accuracy and outperforms existing machine learning models, making it a practical tool for rapid, cost-effective leaf disease detection. | ||
| Keywords | ||
| Cucumber leaf disease, deep learning, convolutional neural network (CNN), image classification, precision agriculture, plant disease detection, data augmentation | ||
|
Statistics
Article View: 11
|
||
