CNN-Random Forest Hybrid Model for Improved Ginger Leaf Disease Classification

The abstract summarizes the key findings and performance indicators of the customized model that is intended for the categorization of illnesses affecting Gingerleaves. Utilizing criteria such as precision, recall, and F1-score, the model exhibited remarkable efficacy in discerning a range of ailments, such as Downy and Powdery mildew, Anthracnose, Angular Leaf Spot, GingerMosaic Virus, Bacterial Wilt, and Target spot. The precision numbers, which range between 95.85% to 98.04%, demonstrate the model's ability to precisely identify cases of each condition and demonstrate its accuracy in reducing false positives. At once, recall values from 96.01% to 97.74% underscore how good it is at spotting diseases and avoiding false negatives. Overall, the model performs a fantastic 96.79
^%
in the classification of Gingerleaf diseases in a dataset that had 7583 images, while the combined F1-scores of precision and recall are balanced as they go from 96.14% to 97.42%→ Harmonized F1-scores, reflecting just the great the overall performance of the model. With a weighted average and macro-average of 96.80%, its stellar 99% Accuracy does nothing but help with the confidence that it will be just as consistent across many illness classes. The impressive recent outcomes illustrate how well the model worked at distinguishing between unhealthy and healthy Gingerleaves, which is crucial to making sure it's a valuable tool for precision disease management in agriculture. What's more, the abstract indicates just how useful and adaptable the model is as “the weighted and macro average for all disease scenarios” continues to perform well. Overall, however, it's clear that by giving the class of diseases that affect Gingerleaves a precise and reliable model for their categorization, this work will make a significant contribution to sustainable farming development as it says. This is another way of saying that this model has broken new ground to provide a state-of-the-art - and highly accurate and consistent-way to control diseases very quickly in agricultural settings.