DeepLeafNet: An Intelligent Deep Learning Framework for Plant Disease Detection in Smart Agriculture

The increasing global demand for food and the adverse effects of climate change have made early and accurate detection of plant diseases a crucial component of smart agriculture. Traditional manual inspection methods are time-consuming, prone to human error, and inefficient for large-scale monitoring. The use of deep learning offers a transformative solution through automated, image-based disease detection capable of handling complex visual patterns and large datasets. However, challenges persist in developing generalizable and explainable models that perform effectively across multiple crops, environments, and disease types. Existing approaches for plant disease detection often lack scalability and accuracy when deployed in diverse field conditions. The unavailability of large annotated datasets and model overfitting to controlled laboratory data restrict their real-world applicability. This study aims to analyze deep learning models suitable for plant leaf disease detection, identify dataset and feature extraction challenges in agricultural imaging, evaluate architectures for multi-crop disease recognition, and propose strategies for integrating deep learning frameworks within precision agriculture ecosystems for real-time and intelligent monitoring