A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Abstract

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.