Research on Crop Disease Image Recognition Based on Internet of Things Technology and Stacking Integrated Learning

Abstract

In the field of agriculture, disease control and management have been a hot research topic of great interest. In recent years, with the reduction of the cost of image sensors and the improvement of the accuracy of deep-learning algorithms, various information processing methods have been widely used in agricultural production. In this paper, an in-depth exploration of crop disease image recognition methods based on IoT technology is carried out. Initially, an innovative method of deploying sensor nodes within an irregular triangular grid is designed to facilitate effective data collection. Subsequently, accurate image segmentation and feature extraction were executed on the accumulated data. A two-tier Stacking framework was used to integrate three lightweight convolutional neural networks. The first level classifier is used to generate data output values for model training; the second level classifier learns further from the output of the first level classifier, corrects the bias of each individual learner in the framework, and produces the final prediction. On the publicly available PlantVillage data set, the EMNet integration model proposed in this thesis has an accuracy of 98.96%, which is at least 0.68% better than other influential DCNN validation accuracies, with good robustness and generalization.