Fruitful Fusion: CNN-Random Forest Synergy in Banana Ripeness Detection

This research paper introduces a new methodology for detecting the maturity of bananas by combining Convolutional Neural Networks (CNN) with Random Forest. The objective is to tackle the pressing need for automated evaluation of fruit quality in the field of agriculture. The experimental configuration included the use of Windows 11 as the operating system, an i5 processor as the central processing unit, and the Colab platform, which together facilitated efficient testing. The model had a notable overall accuracy rate of 86.97%, indicating its strong categorization skills. Additionally, the F1 score, which is calculated as the harmonic mean of accuracy and recall, exhibited an average value of 87.00, therefore emphasising the model's well-balanced performance. The precision scores showed a range of 81.97% to 91.37%, which signifies the level of accuracy in positive forecasts across various ripeness categories. The use of a hybrid strategy efficiently leveraged the feature extraction capabilities of Convolutional Neural Networks (CNN) and the ensemble learning technique of Random Forest to achieve accurate and dependable classification. The findings indicate a significant development in the precise identification of fruit maturity, showcasing potential implications in enhancing harvest procedures and reducing post-harvest wastage. This research establishes a foundation for the development of enhanced and automated techniques for evaluating the quality of fruits, hence offering potential advantages to the agriculture sector.