An Extensive Analysis of CNN-Based Models for Nutritional Deficiency Identification in Plants

The identification of nutritional deficiencies in plants is critical for ensuring optimal agricultural yield and sustainability. Recent advances in Deep Learning (DL) have revolutionized the field of plant diagnostics, offering precise, scalable, and efficient solutions. Because of their capacity to automatically acquire pertinent properties from raw data, DL models-particularly Convolutional Neural Networks (CNNs)are very useful for detecting nutritional deficiencies. CNNs are great at feature extraction because their layers can automatically recognize patterns in the images. This includes things like changes in leaf color, diseases affecting the leaves, and physical signs of shortages like Potassium (K), Nitrogen (N), and Phosphorus (P). This is why CNNs are so effective: they can automatically identify complicated visual signals in huge image datasets, eliminating the requirement for human interaction or domain knowledge in feature selection. This review provides an extensive analysis of CNN-based models used in nutritional deficiency identification, focusing on methodologies, datasets, evaluation metrics, and future directions.