Remote sensing for agricultural applications: A meta-review

Weiss, Marie; Jacob, Frédéric; Duveiller, Grégory

doi:10.1016/j.rse.2019.111402

Cited by 825 publications

(321 citation statements)

References 312 publications

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“…In situ measurements of these parameters can be a time-consuming, expensive, and biased task. To assist in plant breeding programs [ 2 ] as well as in precision agriculture practices [ 3 ], remote sensing technologies have been used in multiple approaches [ 4 , 5 , 6 ], and, lately, this has expanded with the implementation of UAV (Unmanned Aerial Vehicle) based-data. In recent years, UAV-based images, in conjunction with robust and intelligent data processing methods, are being used as an alternative to the human-visual inspection of agricultural landscapes [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

Castro

Marcato

Polidoro

et al. 2020

Sensors

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Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

Castro

Marcato

Polidoro

et al. 2020

Sensors

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“…This is an important indicator for evaluating the efficiency of agricultural water use. At present, the research methods for crop water use efficiency are divided into the following categories: site observation [21][22][23][24], statistical analysis [25,26], model simulation [27][28][29][30] and remote sensing inversion [31][32][33][34]. Based on field observation research, the calculation of crop water use efficiency is more accurate, but the number of sample points is limited, and the sample selection process is strongly influenced by human factors.…”

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confidence: 99%

Temporal and Spatial Changes in Crop Water Use Efficiency in Central Asia from 1960 to 2016

2020

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Water resources among five Central Asian countries are distributed unevenly. Since the collapse of the Soviet Union, the conflict between water and land use has become increasingly serious. Due to limited data, the temporal and spatial characteristics and trends of crop water use efficiency in Central Asia over the past 60 years remain unclear. This paper combines state-level agricultural statistics data and cultivated land data (1975, 2005 and 2015) from remote sensing imagery and calculates crop water use efficiency based on the FAO crop coefficient method. The results are as follows: (1) the development of crop cultivation in Central Asia is divided into an expansion period (1960–1990), a reduction period (1990–2000), and a recovery period (2000–2016); (2) the grain yield in Central Asia increased from 0.9 to 1.9 t/ha during 1960–2016, with Uzbekistan having the highest, reaching 4.2 t/ha in 2016. Cotton yield increased during 1960–1990 and decreased from 1990 to 2016. (3) The grain water use efficiency in Central Asia increased from 0.22 kg/m3 to 0.39 kg/m3 during 1960–2016. The cotton water use efficiency increased from 0.23 kg/m3 to 0.30 kg/m3 during 1960–1990, has decreased since 1990, and is currently close to the 1960s level.

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“…Too much harm done to communities and long-term civilization has been overlooked due to the need to meet rising human demand, but it is all the more necessary that the pace and effects of agriculture on the environment be considered even as we improve our capabilities to plant and farm better using technology. Remote sensing technologies provide opportunities to track the suitability of the environment for agriculture and at the same time take a pulse of the sustainability of the environment in use (Weiss et al 2020).…”

Section: Remote Sensing Imagery and Environmental Datamentioning

confidence: 99%