Published Paper Details:

Agriculture plays undeniably an indispensable role to human kind. Due to increased population, demand for the food is also getting increased, hence the production should be maximized. To ensure this, crops should be protected from diseases that are caused by fungi, viruses and bacteria. Deep learning is a part of a broader family of machine learning methods based on Artificial Neural Networks with representation learning. It can draw conclusions from various sets of raw data. It can aid farmers to predict the yield and quality of crops, detect weed and diseases. Precision Farming applies real time and historical data along with machine learning and deep learning algorithms to arrive at specific decisions for small regions of the application rather than applying the same working for a large range in the traditional method. In order to increase the production and maximize the profit, the crops should be protected from the diseases. In existing system, only the fungal disease called Anthracnose in Mango leaves is detected. For the pre-processing of leaves, histogram equalization method for contrast enhancement and for resizing the image to a standard size central square crop method are used. And for the classification multilayer convolutional neural network is used. In the proposed work, diseases for crops like paddy, plants like brinjal and tress like citrus family will be detected. Grayscale conversion is to be done for the preprocessing and noises in the image will be removed by using median filter algorithm.For the leaf segmentation Active Contour approach is going to be implemented. For the classification part, Multilayer Convolutional Neural Network (MCNN) is going to be implemented. And the crucial improvement in our work is recommending the fertilizers and pesticides for the affected diseases.