Enhancing Rice Crop Health Assessment: Evaluating Disease Identification with a CNN-RF Hybrid Approach

Abstract

This research builds on a new method by using a convolutional neural network (CNN)-random forest (RF) hybrid model to augment the assessment of rice crop health. Aimed at addressing the critical challenge of disease identification in rice crops, the model focuses on the classification of four prevalent diseases: rust, Hispa, rust, and mildew. Making use of the combined abilities of CNN for feature extraction and RF for classification, the suggested hybrid model exhibits a higher accuracy and efficiency rate compared to the previous disease diagnosis methods and single machine learning (ML) models. The approach employed an extensive dataset of annotated rice crop images, the study enumerates the intricate process that features data collection, data preprocessing, feature extraction, and classification. The CNN-RF hybrid model scored a 93.5% accuracy rate, far more effective than the standalone CNN and RF models, which were at 88.2% and 86.7% respectively. This revolution manifests the prospects that exist with the blending of different machine-learning techniques that completely revitalize agricultural disease identification processes. The issue raised by this research is very important for existing agricultural practices, and even more so when we consider the issue of rice cultivation techniques in particular. The machine learning model performs an essential function of early and accurate disease detection which decreases losses of crops and thereby nutrition security across the world. This study is not only a groundbreaking step in the AI application for agriculture but also paves the way for a new research alley to build more effective and universal disease identification systems. This research brings to the fore the symbiosis of computational invention with agricultural know-how, which presents scalable prescriptions to address one of the key problems in world agriculture. This research adds to the growing body of knowledge on the use of machine learning in enhancing crop health assessment and management, on the road into the future of advanced crop management.

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