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Research Project: Advancement of Sensing Technologies for Food Safety and Security Applications

Location: Environmental Microbial & Food Safety Laboratory

Title: Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances

item OMIA, EMMANUEL - Chungnam National University
item BAE, HYUNGJIN - Chungnam National University
item PARK, EUNSUNG - Chungnam National University
item Kim, Moon
item BAEK, INSUCK - US Department Of Agriculture (USDA)
item KABENGE, ISA - Makerere University
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Remote Sensing
Publication Type: Review Article
Publication Acceptance Date: 1/1/2023
Publication Date: 1/6/2023
Citation: Omia, E., Bae, H., Park, E., Kim, M.S., Baek, I., Kabenge, I., Cho, B. 2023. Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances. Remote Sensing. 15:354.

Interpretive Summary: An overview is presented of the current state of the art for remote sensing technology utilizing spectral imaging for field crop monitoring. First addressed are fundamental principles of the remote sensing and spectral imaging. Next, multiple approaches for acquiring spectral field data are reviewed, including ground-, air-, and space-based platforms and the imaging techniques used; and then various methods of spectral data processing and analysis are examined. Important field crop monitoring applications of spectral imaging are presented, as well as the combination of spectral imaging with LiDAR which, as one of many possible implementations of multi-sensor data fusion, combines complementary remote sensing techniques to achieve significant improvements in accuracy over that which is possible from any single technique alone. This review presents a wealth of information useful to agricultural researchers and industrial users of remote sensing for understanding and using current spectral imaging technologies to the greatest possible advantage in essential field crop monitoring applications to help optimize agronomic inputs (such as fertilizer, pesticides, seed, and water) to improve productivity and reduce adverse effects of climate change based on timely in-field data for making decisions and taking effective actions during crop production.

Technical Abstract: Key elements underpinning food security require the adaptation of agricultural systems to support productivity increases while minimizing inputs and the adverse effects of climate change. Advances in precision agriculture over the past few years have significantly enhanced the efficiency of applying spatially variable agronomic inputs, such as fertilizers, pesticides, seeds, and water for irrigation. These advancements have been attributed to increasing innovations that utilize new technologies capable of monitoring field crops for varying spatial and temporal changes. Remote sensing technology has been earmarked as the primary driver of success in precision agriculture, along with other technologies such as the Internet of Things (IoT), robotic systems, weather forecast, and global positioning system (GPS) technology. More specifically, multi-spectral imaging (MSI) and hyperspectral imaging (HSI) have made possible the monitoring of field crop health to aid decision-making, as well as the application of spatially and temporally variable agro-inputs. Furthermore, the fusion of remotely sensed multi-source data—for instance, HSI and light detection and ranging (LiDAR) data fusion—has even made it possible to monitor changes in different parts of an individual plant. In this regard, we review the state of the art of research papers concerning remote sensing technology for field crop monitoring using spectral imaging. An overview of the fundamental principles underlying spectral imaging for remote sensing applications is presented. Following the survey, we provide a chronological discussion of data acquisition, processing, analysis, and applications. Finally, we highlight recent advances made in enhancing crop monitoring through the fusion of HSI and LiDAR data.