Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection.

A country's economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)- based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Smallmodel is utilized as a backbone that generates refined, discriminative, and prominent features.Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning (ML) and DL methods. [ABSTRACT FROM AUTHOR]