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Design and application of intelligent data acquisition system for agricultural experiment

Li Jun;Zhu Shangshang;Tong Yi;Ren Qiancheng;Huang Jie;Hu Yu;Li Jiajia;School of Agronomy,Anhui Agricultural University;  
[Purpose] In order to quickly and accurately obtain field plant phenotypic data with large volume,variety and complexity,variability,and strict requirements for authenticity in agricultural research experiments. [Method] Based on the Internet of things and big data technology, this paper constructs a big data acquisition application system in agricultural experiment,which is used to assist manual test data acquisition and application. The Arduino development board was used to obtain the environmental data in agricultural experiments. The image of the test area was collected by the raspberry pie connected camera,and uploaded to the database based on the internet of things equipment. Finally,the plant morphology of crops was scanned by the terminal equipment,and then the data from different sources were cleaned and processed in different ways. The original data and the data after cleaning were stored in different data areas and solidified, and further operations were carried out through the distributed file system HDFS(Hadoop distributed file system). The data processing module monitors and processes the data and submits the results to the front-end interactive website in the form of images,tables and videos.[Result] In this paper,5 450 soybean leaf images obtained by the system were used to train the deep learning model based on yolov5,and finally the classification and recognition of soybean leaf shape were realized. The plant height measurement equipment was used to measure the plant height of 1 306 soybean plants,and decent results were obtained.[Conclusion] The research shows that the design scheme of the system has the characteristics of high feasibility,wide application,low construction cost and strong expansibility.It applies various technologies to the data acquisition of agricultural experiments, standardizes the experimental process and data storage,improves the breadth of data acquisition and the depth of data utilization,and lays a foundation for deeper agricultural scientific research.
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