AN EFFICIENT EDGE-BASED PLANT DISEASE DETECTION MODEL USES AN ENRICHED DATASET AND DEEP CONVOLUTIONAL NEURAL NETWORK

Abstract

Crops and yields are significantly harmed by plant diseases, one of agriculture’s most significant problems. Researchers have recently investigated using artificial intelligence (AI) to detect and effectively manage disease early on to address this issue. This research focuses on developing a method to optimize the DCNN (Deep Convolutional Neural Network) classification model for plant diseases.  We enriched the data by incorporating data from two public datasets, PlantVillage Dataset (PVD) and CroppedPlant Dataset (CPD), and we trained the model using two-step transfer learning. The experimental results demonstrate that the model’s accuracy is 82%, more significant than previous studies. Notably, achieving this result with fewer parameters while maintaining adequate performance compared to previous research demonstrates the model’s efficient use of limited computing resources. Hence, the proposed model is deployable on edge devices to optimize availability and efficiency in real-world environments and contribute to deploying new edge computing and agriculture services.