Content of Agricultural Engineering in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Reference Manager ProCite BibTeX RefWorks Toggle Thumbnails Select A study on the factors influencing the use of agricultural APP by farmers based on canonical correlation analysis QIAO Cheng-huan, QIAO Cheng-shuo, WANG Dong-qi, CUI Xian, CONG Lei HUBEI AGRICULTURAL SCIENCES    2023, 62 (11): 159-164.   DOI: 10.14088/j.cnki.issn0439-8114.2023.11.028 Abstract （76）      PDF （1592KB）（27）       Knowledge map    Save In order to compare the importance of various influencing factors in the promotion process of agricultural APP, farmers in Yantai City, Tai’an City, and Weihai City, Shandong Province were selected as the research objects. Typical correlation analysis methods were used to analyze subjective factors and objective factors on farmers’ use of agricultural APP. The results indicated that the degree of understanding of the software among subjective factors was the main factor determining whether to use agricultural APP, while whether traditional information channels met information needs was a secondary factor. When traditional information channels could not meet farmers’ information needs, it was beneficial for the promotion of the software;the average monthly frequency of obtaining technical guidance and the age of farmers were the main and secondary factors in objective factors, respectively. In summary, when promoting agricultural APP, the focus should be on farmers over 40 years old, and on areas where traditional information channels had developed well but couldn’t fully meet farmers’ needs for agricultural information. Emphasis should be placed on cultivating farmers’ software usage abilities. Reference | Related Articles | Metrics Select Research progress in crop disease and pest identification based on deep learning LI Zheng, LI Bao-xi, LI Zhi-hao, ZHAN Yi-fang, WANG Li-hua, GONG Qi HUBEI AGRICULTURAL SCIENCES    2023, 62 (11): 165-169.   DOI: 10.14088/j.cnki.issn0439-8114.2023.11.029 Abstract （236）      PDF （1570KB）（200）       Knowledge map    Save In order to effectively prevent and control crop diseases and pests, and ensure crop health, rapid and accurate identification of crop diseases and pests was a prerequisite for effective prevention and control.A review was conducted on the research on crop pest and disease identification, summarizing the development process of crop pest and disease identification methods. The focus was on analyzing the network structure, modeling key links, and six typical architectural features of deep learning. Combined with current research hotspots and application prospects, prospects were made from the construction of public datasets, integration of multiple imaging technologies, and optimization of large model performance. Reference | Related Articles | Metrics page Page 1 of 46 Total 92 records First page Prev page Next page Last page