Multi-Output Vs Single-Output Deep Learning for Plant Disease Detection

Abstract

AI-based image processing plays a crucial role in agriculture by enabling early detection of plant diseases, thereby increasing crop productivity and minimizing economic losses. In this study, we present a comparative analysis between a multi-output deep learning model, which simultaneously classifies plant species and assesses their health status, and two separate single-output models trained for each task individually. The publicly available PlantVillage dataset was used for training and evaluation. Performance metrics such as classification accuracy, F1 score, training time, and confusion matrices were used to assess each model. Our results indicate that the multi-output architecture achieves remarkably high classification performance (Plant: 99.98%, Health: 99.78%) while significantly reducing training time by nearly 50% compared to the combined cost of training two individual models. This demonstrates that a unified model not only provides computational efficiency but also maintains predictive strength, making it a practical alternative for real-time agricultural decision support systems. The findings suggest that integrated modeling can contribute to the development of scalable, resource-efficient solutions in precision agriculture. © 2025 IEEE.