Using Machine Learning and RGB Images to Assess Nitrogen and Potassium Status in Sorghum (Sorghum bicolor L.) Under Field Conditions

Sorghum (Sorghum bicolor L.) is a resilient crop with high relevance in tropical and semi-arid regions, where nutritional deficiencies, particularly of nitrogen (N) and potassium (K), limit yield. This study evaluated the potential of RGB imagery combined with machine learning to detect N and K deficiencies in sorghum at different phenological stages. The traditional models showed significant limitations in distinguishing nutritional status, especially at the early V4 stage, where accuracies remained below 40%. At the flowering stage, their performance improved for nitrogen detection, reaching up to 58% accuracy, but remained insufficient for potassium (below 30%). In stark contrast, the CNN demonstrated substantially superior performance, effectively identifying even subtle visual symptoms. For nitrogen deficiency, the CNN achieved high accuracies of 76% at the V4 stage and 87% at flowering. While potassium classification proved more challenging overall, the CNN still outperformed traditional models, reaching 55% accuracy at flowering. These results indicate that deep learning is a powerful and viable low-cost tool for the early and accurate diagnosis of nutrient deficiencies in sorghum, overcoming the limitations of conventional machine learning approaches.