Enhancing crop yield prediction through multi-modal ensemble learning with IoT data integration using AgroNetVision

Abstract

Early and accurate Crop Yield Prediction (CYP) becomes the most crucial input in optimizing agricultural strategy as well as income generation for farmers. Crop classification, area estimation, and health monitoring are some applications of recent developments in deep learning, showing great potential in all aspects of agricultural activities. IoT technology further helps track resources and monitor real-time data in smart farming, ensuring quality production by streamlining such farming operations. It deploys AgroNetVision, an innovative ensemble learning model which effectively integrates IoT data to predict crop yields accurately. It relies on the near-real-time imagery captured by IoT devices using the YOLO architecture for visual detection of crop conditions but concurrently uses LSTM on time-series soil sensors and environmental monitor data to capture temporal dynamics. Now, the AdaBoost will receive the combined and processed features from both streams of visual and sensor data to boost predictability. The ensemble learning architecture is developed based on the aggregation of the output produced by the YOLO, LSTM model, and AdaBoost to provide an efficient yield prediction. The proposed model is strictly compared with the state-of-the-art approaches in order to test its efficacy. AgroNetVision shall apply a multi-source data integration approach coupled with advanced learning techniques to provide actionable insights for the farmer for more informed decisions and the maximization of crop yields in dynamic agricultural environments.