An Empirical Study of Precision Agriculture

The demand for food production has led to advancements in precision agriculture, aiming to enhance crop yield and quality. This study investigates the application of deep learning algorithms, including GoogLeNet, RESNET-50, MobileNet-v2, VGG-16, and ShuffleNet, for automated plant disease detection. The research utilizes a dataset comprising images of citrus diseases to train and evaluate the models. Results show promising accuracy rates, highlighting the potential of deep learning in optimizing resource utilization and facilitating timely interventions in agriculture.