In agriculture, it is necessary to put fertilizer, which is conducive to the growth of crops. However, the amount of fertilizer must be strictly controlled, too much and too little will become the obstacles to the growth of crops. With the deepening of agricultural automation, the requirements of fertilizer put are more and more strict, so effective monitoring of the uniformity of fertilizer put is conducive to the good growth of crops. Aiming at the problem of fertilization uniformity detection, this paper designs an intelligent image detection system based on Internet of things technology. According to the actual needs, the system includes three modules: image acquisition, image transmission and image processing. Among them, the image acquisition module uses sensors to collect the land image after fertilization, the image transmission module uses network technology to transmit the collected image to the cloud, and the image processing module uses the good performance of convolution neural network to effectively extract the image characteristics and identify whether the uniformity of image fertilization is reasonable or not after image preprocessing. Through the simulation analysis, it shows that the intelligent image detection system based on the Internet of things can well detect whether the uniformity of fertilization is reasonable, improve the yield of crops, and promote the sustainable development of agricultural health.

Xuebing Liu. Design and Implementation of Intelligent Image Detection System for Fertilizer Uniformity Based on Internet of Things. Distributed Processing System (2022), Vol. 3, Issue 2: 14-26. https://doi.org/10.38007/DPS.2022.030202.

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