Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops.
LIDAR
agriculture
artificial intelligence
disease detection
electro-optics
fluorescence
food crop
heath assessment
hyperspectral
laser
machine learning
multispectral
precision agriculture
remote sensing
sensor
spectroscopy
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
29 Dec 2020
29 Dec 2020
Historique:
received:
06
12
2020
revised:
23
12
2020
accepted:
24
12
2020
entrez:
1
1
2021
pubmed:
2
1
2021
medline:
9
3
2021
Statut:
epublish
Résumé
In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
Identifiants
pubmed: 33383831
pii: s21010171
doi: 10.3390/s21010171
pmc: PMC7795220
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Food Agility CRC
ID : FA042
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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