Prediction and Analysis of Paddy Crops Disease in Artificial Intelligence Techniques

The survival of human beings is generally based on the proper productivity of agriculture. The paddy plant is considered as a major planting crop in improving the economical level of our country. Nowadays, the yield level of paddy crop might be minimized due to several diseases. Bacteria, fungi, virus and certain harmful insects are the main causative agents for such disease occurrence on the paddy crop. The diseases which affect the early stage of the paddy crops influences in the whole stage of crop cultivation. In early days of agriculture, the manual detection of diseases has been carried out by farmers. Image processing is one of the emerging techniques for identifying and classifying the different types of diseases and it overcomes the issues encountered during the manual detection of diseases. Image processing technique solves several issues involved in the cultivation of crops including, recognition and classification of plant diseases, discrimination of certain weeds and disease forecasting.

At the core of such system would be to automatically recognize the disease that has occurred. We address this problem in this article. We briefly present our approach to solving the problem of automatic detection and classification of rice plant diseases. We collected the leaves from rice farm and prepared a dataset of images of rice plant leaves having a white background. Our system first removes the background from an image and then using K-means clustering it extracts the disease portions of the leaf image. After applying K-means clustering, some unnecessary green region is removed from disease portion using thresholding technique [5].

II. IMAGE PROCESSING
In Image processing section, initially the image is captured from the camera and further the image is processed using k means clustering for segmenting the image. The processed image is then edge detected using three different edge detection techniques. The edge detection techniques used are sobel, prewitt and canny algorithm. The diseased sample banana leaf has been taken for the edge detection analysis. Amongst the three edges detection methods used, canny edge detection algorithm gives the better and reliable detection. Owing to its optimality to meet with the three criteria for edge detection and the simplicity of process for implementation, it became one of the most popular algorithms for edge detection method.
As discussed earlier, IoT and Image processing are combined together in agricultural field in order to increase product yield and to reduce the crop failure.
We focused on plant failure due to environmental factors through IoT technology. IoT system includes sensors, Arduino and a camera that regularly captures the plant. The color, texture, shape and area of the leaf are the parameters also considered in this work.
After examine the conditions of the plants we go for image processing. The initial test is done by using MATLAB software. In addition to the environmental factors, the plant with a diseased leaf can also be identified using Image processing. Based on the output and constraints the pesticides will be sprayed for the crop/plant where the disease is identified. If there is any change that corresponds to the deterioration in the plants growth, the farmer is immediately informed. Early diagnosis will thus help in taking the necessary actions to increase the produce and reduce failure of crops.    When a stimulus hits them, a process takes place in these nodes. Some of them are connected and marked, and some are not, but in general, nodes are grouped into layers. The system must process layers of data between the input and output to solve a task. Creative and analytical components of information are analyzed and grouped to ensure that the object is identified correctly. The creation of neural network is inspired by the working of human brain and its functions. Artificial intelligence and machine learning, which is a subset of AI, play an essential part in its functionality. It starts working when a developer enters data and builds a machine learning algorithm, mostly using the "if ... else ..." principle of building a program. The deep neural network does not only work according to the algorithm but also can predict a solution for a task and make conclusions using its previous experience.

VI. SIMULATION RESULTS
The image dataset is processed using ANN and KNN Classifiers to detect the blast disease in rice crops. The performance of the classifier algorithms is analysed using the common QoS parameters as described in  [5] Anami, B. S., Pujari, J. and Yakkundimath, R.