Botanic Precision: A Hybrid CNN-RF Model for Accurate Weed Disease Classification

For accurate weed disease classification, this paper presents a novel hybrid model that combines Random Forest (RF) and Convolutional Neural Network (CNN) approaches. The model, which cleverly combines the unique strengths of CNN and RF, is carefully tested and produces impressive results on a range of performance criteria. With spectacular accuracy percentages ranging from 86.20% to 94.29%, the CNN-RF hybrid model that is shown is very proficient in reducing false positives while maintaining elevated precision. Meanwhile, recall rates, which range from 84.31% to 94.06%, highlight the model's strong capacity to correctly detect occurrences of the desired weed classifications, hence reducing the likelihood of false negatives. The balanced F1 scores, which range from 88.28% to 94.17%, support the model's sophisticated performance and overall accuracy even further. Support values are real-world examples of each weed class, indicating a large dataset that is essential to the model's thorough training and analysis. The algorithm demonstrates its ability to make accurate predictions over the whole range of cannabis classes, with an overall accuracy ranging from an astonishing 97% to 99%. The suggested CNN-RF hybrid model shows great promise for agricultural diagnostics as an extremely effective tool for classifying weed diseases.