Detecting plant diseases is a critical task in agriculture to ensure the health and productivity of crops. Traditional methods of disease detection are time-consuming and require specialized knowledge. In recent years, deep learning and machine learning techniques have shown great potential for automating the plant disease detection process. This project aims to develop a plant disease detection system using a combination of these techniques. Initially, we trained a convolutional neural network (CNN) model on a large dataset of plant images to classify them as healthy or diseased. We also utilized transfer learning techniques to fine-tune a pre-trained CNN model on a smaller dataset of plant images to detect specific diseases. Furthermore, we applied machine learning algorithms such as decision trees and random forests to identify the key features that distinguish healthy plants from diseased ones. To evaluate the performance of our system, we tested it on a dataset of real-world plant images and achieved high accuracy in detecting various plant diseases. We also compared the performance of our system with traditional methods of plant disease detection and found that our system outperformed these methods in terms of accuracy and speed. In summary, our plant disease detection system, which incorporates deep learning and machine learning techniques, can provide a fast, accurate, and reliable solution for detecting plant diseases. This can ultimately help improve crop yields and contribute to sustainable agriculture practices.
CNN, ANN, K-mean, KNN, Deep learning, Machine learning, Plant Disease, Agriculture.
Vipin Gupta , Prof. Priyanka Parmar "Systematic Review of Different Plant Disease Prediction Techniques Using Deep Learning and Machine Learning." Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 382-388
Vipin Gupta , Prof. Priyanka Parmar "Systematic Review of Different Plant Disease Prediction Techniques Using Deep Learning and Machine Learning." Iconic Research And Engineering Journals, 6(11)