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The Informatization of Agriculture

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1828

Special Issue Editors


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Guest Editor
Agricultural Information Institute, Chinese Academy of Agricultural Science, Beijing 100081, China
Interests: agricultural informatization; agricultural economic management; interdisciplinary integration of technology and methods in geography, statistics and computer science; agricultural spatiotemporal information mining and simulation

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Guest Editor
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: informatization of animal husbandry

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Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: smart agriculture; agricultural robotics; machine learning; deep learning
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Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: remote sensing applications in agriculture

Special Issue Information

Dear Colleagues,

Informatization is regarded as a path to increase the yield and to improve the efficiency of agriculture. Furthermore, it has a profound impact on agricultural production and the daily lives of rural residents. Significantly, information technologies such as big data, Internet of Things (IoT), crop monitoring, robotics, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), geographic information systems (GISs), remote sensing, global positioning systems (GPSs), artificial intelligence, machine learning, blockchains, etc., have been applied to the whole chain of agriculture, including production, management, circulation and services. Moreover, the widespread use of smart phones based on 4G networks has greatly reduced the cost of farmers' information acquisition, promoted the use of agricultural technology, and even profoundly affected the governance of rural societies and rural residents' daily lives.

This Special Issue focuses on the application of information technology in agriculture. In addition to basic research at a technical level, we expect to scientifically and comprehensively evaluate the specific impact of the application of information technology on agricultural production and farmers' welfare. For example, does agricultural e-commerce improve the circulation efficiency of agricultural products? Mobile Internet has revolutionized the way that rural residents access information: what impact does it have on rural governance, agricultural production, and the welfare of farmers? Has the application of IoT and UAVs improved agricultural production efficiency? What role does agricultural remote sensing technology play in yield estimation and disaster prediction?

To further develop these dimensions, we invite authors to submit original contributions, based either on thorough theoretical work or on the analysis of empirical data. Research areas may include (but are not limited to) the following:

  • The digital transformation of rural governance;
  • The impacts of Internet use on agricultural production and rural governance;
  • The e-commerce of agricultural products;
  • The digital supply chain of agricultural products;
  • Blockchain and big data technology in agriculture;
  • Internet of Things (IoT) in agriculture;
  • Crop monitoring based on remote sensing technology.

We look forward to receiving your contributions.

Prof. Dr. Jianzhai Wu
Prof. Dr. Qifeng Li
Prof. Dr. Longsheng Fu
Dr. Miao Lu
Guest Editors

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Keywords

  • Rural Governance
  • Agricultural E-commerce
  • Digital countryside
  • Internet of Things in Agriculture
  • Digital Supply Chain
  • Geographic Information System
  • Remote Sensing Technology
  • Crop Monitoring
  • Blockchain technology

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Published Papers (1 paper)

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Research

20 pages, 13834 KiB  
Article
Multi-Sensor Remote Sensing to Estimate Biophysical Variables of Green-Onion Crop (Allium cepa L.) under Different Sources of Magnesium in Ismailia, Egypt
by Hassan A. Hassan, Emad A. Abdeldaym, Mohamed Aboelghar, Noha Morsy, Dmitry E. Kucher, Nazih Y. Rebouh and Abdelraouf M. Ali
Sustainability 2023, 15(22), 16048; https://doi.org/10.3390/su152216048 - 17 Nov 2023
Cited by 1 | Viewed by 1174
Abstract
Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant [...] Read more.
Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season. Full article
(This article belongs to the Special Issue The Informatization of Agriculture)
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