Fusion of Convolutional Neural Networks and Gradient Boosting Machines for Spinach Leaf Classification and Prediction
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Abstract
The primary objective of this research is to develop a robust model for spinach leaf classification and prediction by fusing Convolutional Neural Networks (CNNs) with Gradient Boosting Machines (GBMs). This study addresses the limitations of current methods, which often rely on manual inspection or simplistic machine learning models, by proposing a hybrid approach that leverages the deep feature extraction capabilities of CNNs and the predictive power of GBMs. High-resolution images of spinach leaves under various conditions were preprocessed and used to fine-tune a pre-trained CNN model, with the extracted features subsequently fed into a GBM for classification and prediction. The integrated model demonstrated significant improvements, achieving an accuracy of 95.3%, precision of 94.1%, recall of 93.8%, and an F1-score of 94.0%, outperforming standalone CNN and GBM models. These results indicate a substantial enhancement in both accuracy and robustness. The proposed fusion model provides a comprehensive and reliable tool for precision agriculture, offering potential for real-time implementation and integration with IoT devices for continuous crop monitoring. This research contributes to advancing agricultural practices by ensuring early detection of diseases and nutrient deficiencies, ultimately optimizing crop health and yield.
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