Elevating Grape Leaf Disease Identification Through Integrated CNN-Random Forest Approach

The abstract provides a thorough summary of a study that examines the effectiveness of various grapevine-related classes using a classification methodology. The model's capacity to precisely forecast positive cases inside classes is demonstrated by the research's precision scores, which range from 68.25% to 77.94%. Recall scores also range from 69.49% to 79.71%, demonstrating the model's skill in identifying genuine positive cases. The range of the harmonised F1-Score, which measures the harmony of recall and precision, is 69.35% to 78.01%. Model evaluations have been solidly grounded in large instances in each class, which vary from 590 to 750. The model's proficiency across all classes is demonstrated by the weighted average accuracy, recall, and F1-Score, each of which is impressively high at 85.40%, 86.00%, as well as 84.90%, respectively. Notably, the model's thorough understanding is demonstrated by the micro average accuracy, remembrance, and F1-Score, which reaches 73.78%. These findings demonstrate how well the model performs in accurately classifying a range of grapevine-related problems, significantly advancing tactics for managing and assessing grapevine health. The study's conclusions could have an impact on viticulture and farming strategies.