EFFICIENT COFFEE PLANT DISEASE PREDICTION WITH ADAPTIVE FEATURE SELECTION AND DEEP CONVOLUTIONAL NEURAL NETWORK

The problem of predicting disease in coffee plant is briefly analyzed and there exist numerous techniques around the issue. However, the approaches struggle to achieve maximum accuracy in predicting possible disease which affects the growth of plant. Towards this, an efficient Adaptive Invariant Feature Selection and Approximation based Disease Prediction with DCNN (AIFSADP-DCNN) is sketched. The method applies Intensity Deviation Normalization technique to normalize the leaf image and enhance the image quality Further, Color Quantization Segmentation algorithm is enforced towards segmenting the coffee plant features. Next, color, texture, and distribution features are extracted from the segmented image. Concern features extracted are trained with deep convolution neural network with three layers of convolution and pooling layers. The output layer neurons are designed to measure Color Constraint Support (CCS), Texture Constraint support (TCS) and Distribution Constraint Support (DCS) towards various classes of features. Finally, the method estimates plant disease support (PDS) towards various classes based on which the method identifies the disease class. The proposed model improves the performance of disease prediction in coffee plants with higher accuracy up to 96.5%.