ENHANCING COFFEE LEAF DISEASE DETECTION WITH RMFA-CNN: A REAL-TIME MULTI-FEATURE DEEP LEARNING FRAMEWORK

Disease prediction in coffee plants has been widely investigated with several approaches utilizing diverse features and measures. However, existing methods often fail to achieve precise classification and are affected by high false prediction rates. The research problem has more impact on the crop of plants and achieving higher yields. The research problem is contributed with a novel approach which incorporates multiple features like intensity and texture of the leaf image towards prediction. Also, the model is designed with three levels of convolution layers to reduce the feature size and supports maximizing classification accuracy. To overcome these limitations, we propose a Real-Time Multi-level Intensity Feature Analysis based Convolutional Neural Network (RMFA-CNN) for efficient disease prediction in coffee plants. The model focussed on handcraft features to be extracted with dedicated schemes of preprocessing, segmentatitaon and feature extraction where the classification is performed with CNN. In the proposed framework, plant images are first pre-processed using a region centric diagonal normalization algorithm which traverses the entire image and enhances visual quality based on intensity features. Subsequently, a gray covariance segmentation algorithm is applied to partition the image into regions according to gray level characteristics. From the segmented regions, colour and texture features are extracted and transformed into a unified one dimensional feature vector for deep CNN training. During testing, the model estimates Intensity Disease Support (IDS) and Texture Disease Support (TDS) which are further combined to compute the Disease Class Support (DCS). Based on the DCS values, the system accurately predicts the disease class. The method is evaluated with two publicly available Arabica coffee leaf datasets, namely JMuBEN and JMuBEN2, which were acquired under real-world conditions at the Mutira coffee plantation in Kirinyaga County, Kenya. Experimental results demonstrate that the proposed RMFA-CNN significantly improves classification accuracy up to 98.6 and reduces false predictions thereby enhancing the reliability of coffee plant disease prediction.