Leaf disease detection using CNN-Deep learning Project
In this Image processing project a deep learning-based model is proposed ,Deep neural network is trained using public dataset containing images of healthy and diseased crop leaves. The model serves its objective by classifying images of leaves into diseased category based on the pattern of defect.
Platform : Matlab
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Agricultural productivity is a key component of the Indian economy. Therefore the contribution of food crops and cash crops is highly important for both the environment and human beings. Every year crops succumb to several diseases. Due to inadequate diagnosis of such diseases and not knowing symptoms of the disease and its treatment many plants die. This study provides insights into an overview of plant disease detection using different algorithms. A CNN-based method for plant disease detection has been proposed here. Simulation study and analysis are done on sample images in terms of time complexity and the area of the infected region. It is done by image processing technique. A total of 15 cases have been fed to the model, out of which 12 cases are of diseased plant leaves Test accuracy is obtained as 89.80%.
India is a country with a population of approximately 1.38 billion as of April 2020. Estimates put the total number of farmers in India somewhere between 95.8 million. It must be noted that 18% of India’s GDP is produced from the agricultural sector. It would, thus, be safe to infer that if agriculture was revolutionized, it’d benefit the country greatly and also apart from alleviating the conditions of local farmers, it’d also create a lot of employment and expansion opportunities in the agricultural sectors. Research and development on pesticides, fungicides, and herbicides have progressed very well in India. But, every year, due to natural reasons, crops succumb to various known diseases and tonnes of produced corps are lost and this can be dealt with quick detection of plant diseases in proper time. It will help to get over the dire economic conditions faced by the country’s farmers. Nowadays technology has changed lives for the better. Due to the internet, almost everything is within reach. With the help of a normal camera, one can easily click photos of affected parts and upload it to the system which detects the particular disease and provides the exact treatment as well as a pesticide if required. Most of the plants are infected by various fungal and bacterial diseases. The exponential increase of population, the climatic conditions also cause plant diseases. The leaves require close monitoring to detect the disease. There are several techniques are reported by many researchers for plant disease detection and monitoring.The proposed system is consist of two parts. One is extraction of disease feature and the other one is classification of the same. Healthy plant with no disease ,rust, scab, downy and mildew are the five classes are considered in this work. With this variations of grape leaf diseased images the proposed system’s classification performance is examined in terms of accuracy and a desirable score of 90% is obtained for the same.
In this image processing project, a deep learning-based model is proposed to solve the problem. The deep neural layer is trained using a public dataset containing images of healthy and diseased crop leaves. The model serves its objective by classifying images of leaves into diseased categories based on the pattern of the defect. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides the way to solve this problem.
- Easy to detect disease when compared with different image processing techniques.
The proposed algorithm is implemented successfully to train the system. The accuracy percentage on the test set is 89.80% with no overfitting There is still room for improvement as the remaining 11.20% is covered. This present work can contribute to agricultural domain and can be used to help people for tracking their house plants and also enables the farmers to keep a track of the harvest. This work can be expanded into further to develop an app through which one would also know the remedy to a plant disease
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