A Deep Learning Model for Detection and Classification of Nutritional Deficiency in Coffee Plant

Coffee is one of the most popular beverages consumed worldwide and is also an important economic driver in agricultural economies. However, nutritional deficiencies in coffee plants have a major effect on the quality and yield of the crop. Detection of these deficiencies early and accurately is critical for effective intervention and management. In this work, we introduce a novel deep-learning framework for detection and classification of nutritional deficiencies in coffee plants. DenseNet-201, AlexNet, and MobileNet-V2 are integrated to extract discriminative features from coffee leaf images, and an attention-based feature fusion mechanism is proposed using squeeze-and-excitation blocks to improve feature representation. A differential evolution algorithm is used to optimize a Kernel extreme learning machine for learning efficiency and generalization to classify the extracted features. A benchmark dataset is used for the evaluation of the proposed model and its performance is assessed against multiple performance metrics such as accuracy, precision, recall, specificity, F1-score, and the Matthews correlation coefficient. The proposed method is compared with existing deep learning models, and it is found that the proposed method outperforms the other models with a classification accuracy of 99.50%, precision of 99.22%, recall of 99.24%, specificity of 0.9947, F1-score of 0.9957, and M correlation coefficient of 0.9908. The model is able to identify nutritional deficiencies accurately, and these results confirm the model’s effectiveness as a practical and scalable solution for precision agriculture and sustainable coffee cultivation.