Diagnosis of Nutritional Deficiencies in Coffee Plants Through Automated Analysis of Digital Images Using Deep Learning in Uncontrolled Agricultural Environments

This study aimed to develop a deep learning-based application for the automatic detection of nutritional deficiencies in coffee plants through the analysis of in-field leaf images. Images were collected from farms in the Shipasbamba district and classified into six deficiency types: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe). A total of 2643 leaves were labeled and preprocessed for model training. Several YOLO architectures were evaluated, with YOLO11x achieving the best performance after 100 epochs, reaching a precision of 88.98%, recall of 88.54%, F1-Score of 88.76%, and mAP50 of 92.68%. An interactive web application was developed to allow real-time image upload and processing, providing both graphical and textual feedback on detected deficiencies. These results demonstrate the model’s effectiveness for automated diagnosis and its potential to support coffee growers in timely, data-driven decision-making, ultimately improving nutrient management and reducing production losses.