ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) MODEL TO PREDICT THE DISEASE SEVERITY OF RICE

Rice is the important crop around the world. The rice crop is affected by various diseases. Among them the sheath rot disease is the most devasting disease and makes major challenge to the rice cultivation. This paper presents a model designed using Adaptive Neuro-Fuzzy Inference System (ANFIS) to diagnosis the disease severity in rice. ANFIS helps to determine the incompleteness in decision making made by human expert using the learning mechanism. Fuzzy inference system and neural network are combined in ANFIS, the input parameters are passed through input layer and output could be viewed through output layers. Training is involved with iterative adjustment of parameters of the ANFIS using hybrid learning process to diagnosis the disease severity of rice. ANFIS uses five layers, each layer has its own nodes. Layer1 has the input variables with membership function. Layer2 uses T-norm operator which uses AND operator. The rules are added and fired are assigned to layer3. Layer4 nodes are adaptive and consequent parts of the rules are performed. Single node that computes the overall output in layer5. With the input parameters Number of discoloured grains/panicle, Number of chaffy grains/panicle, Lesion Number/tiller, Lesion size (mm)-Length& width and Number of panicles infected/tiller, the algorithm is developed to diagnosis the disease severity. The proposed Fuzzy Prediction Model is effectively “hand crafted” to achieve the desired performance and also used for diagnosis disease severity