Precision in Cassava Root Disease Diagnosis: Merging CNN and Random Forest Techniques

Their abstract presents a novel method for identifying cassava root disease by creating a sophisticated model. Using Convolutional Neural Networks (CNNs) in combination with Random Forest, the model achieves high recall, high precision, and F1-Score values of more than 97.7% for a few disease classes: cassava Mosaic Disease, cassava Brown Streak Disease, cassava Bacterial Blight, cassava Anthracnose Disease, or cassava Root Rot. The macro-, weighted--, & micro-average accuracy metrics, which add up to an astounding 97.80%, all demonstrate how resilient the model is. With its thorough insights into the model's classification performance, Figure 3 offers a nuanced breakdown of true positives, false positives, false negatives, and true negatives. Furthermore, the complex CNN architecture behind this achievement is explored in Table 3. The research involved examining a dataset of 6810 images. The model demonstrated impressive performance, achieving an accuracy of 97.79% in overall classification. The model's dependability across a range of disease cases is highlighted by these metrics, even though macro, weighted, and micro-average support and support proportion values were missing. The cumulative effect of these results highlights the revolutionary effect of the model on the identification of diseases in cassava production. Enhancing crop health and advancing precision agriculture is made possible by the precision and accuracy of the methodology. The useful tool for farmers to maximize cassava production and secure a more secure and resilient agricultural future, this creative integration of cutting-edge technologies not only transforms disease identification but also makes a significant contribution to the larger goal of sustainable agricultural practices.