Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect, classify the diseases in paddy leafs. Paddy leaf Diseases Classification done using Convolutional Neural Network (CNN) classifiers. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging. This project is developed in python.
The ability to identify the fruits based on the quality in food industry is very important nowadays where every person has become health conscious. There are different types of fruits available in the market. However, to identify best quality fruits is cumbersome task. Therefore, we come up with the system where fruit is detected under natural lighting conditions. The method used is texture detection method and shape detection. For this methodology, we use image processing to detect particular eight type of fruit. This fruit detection project is implemented in matlab using image processing.
Diabetic Retinopathy (DR) is a chronic health disease which requires early detection and treatment. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Diabetic Retinopathy is a consequence of diabetes that affects the eyes. Damaged blood vessels in the retina, a light-sensitive tissue, are the primary cause of DR. Patients with Type 1 or Type 2 diabetes are more likely to have this condition. If the patient has a long-term case of diabetes and the blood sugar level is not regulated consistently, the odds of this issue developing in the eye increase. Diabetic Retinopathy is one of the most common causes of blindness in the Western countries. Preventing Diabetic Retinopathy has found to be quite beneficial when people with diabetes are monitored regularly. This process is shown to be essential if Diabetic Retinopathy is discovered in its early stages due to the availability of treatment. Diabetic Retinopathy, the main cause of blindness among working-age adults, affects millions of individuals. Diabetic Retinopathy is a medical disorder in which diabetes mellitus causes damage to the retina. Diabetic Retinopathy is diagnosed using colored fundus images, which requires trained clinicians to recognize the presence and importance of several tiny characteristics, making it a time-consuming task. We present a deep learning based technique to detect Diabetic Retinopathy in fundus images in this project. This project is developed in matlab.
Grapes is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a image processing model. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is 98 % in this project. This project is developed in python.
