Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

Schwalbert, Raíssa; Amado, Telmo Jorge Carneiro; Corassa, Geomar Mateus; Pott, Luan Pierre; Prasad, P. V. Vara; Ciampitti, Ignacio A.

doi:10.1016/j.agrformet.2019.107886

Cited by 258 publications

(115 citation statements)

References 29 publications

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“…Finally, micro/nanosatellites can provide data in several spectral bands (which are sensitive to the crop structure, leaf pigment, and water content), and they enable meter-levelresolution images for estimating crop phenotyping traits at a very large scale [24], [79], [87], [88], [144], [193], [236], [239]. Micro/nanosatellites include optical and synthetic aperture radar (SAR) satellites.…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

“…Optical satellites use the relative reflectance at certain spectral wavelengths to estimate crop phenotyping traits based on VIs, machine learning methods, weather data, environmental factors, and crop varieties [79], [193], [236], [289]. However, optical satellites have saturation problems for VIs and a reduction in their estimation accuracy at high plant densities [24].…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

“…For satellite-based platforms, Sakamoto [234] incorporated MODIS VIs and environmental variables to estimate the yields for maize and soybeans using a RF regression algorithm, and their results indicated that the RF method obtained a better yield estimation accuracy (maize RMSE = 0.539 ton/hectare and soybean RMSE = 0.206 ton/hectare) at the state level. Similarly, Schwalbert et al [236] integrated long short-term memory (LSTM) neural networks, weather data, and machine learning for improving crop yield prediction in southern Brazil and showed that LSTM neural networks performed better than other algorithms. In addition, Dong et al [145] estimated wheat yield using a light use efficiency model and wheat variety data, and the results indicated that the proposed method enhanced the model simulation performance and achieved an 82% estimation accuracy of the interannual yield variation.…”

Section: Crop Yield Estimationmentioning

confidence: 99%

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High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Jin

Zarco‐Tejada

Schmidhalter

et al. 2021

IEEE Geosci. Remote Sens. Mag.

177

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 0274-6638/20©2020IEEE IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MONTH 2020 C rop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields. BACKGROUND Worldwide demand for food will increase through 2050 and beyond due to the increasing global human population. This represents a huge challenge to crop researchers and agricultural policymakers because current yield gain rates will not be sufficient for the demands of population growth, while climate change will make the difficulty even greater. Today's DNA sequencing, marker-assisted breeding, transgenic technology, genome-wide association study (GWAS) approaches, and quantitative trait loci (QTL) identification have been applied, to a limited extent, to improve crop yields [1]-[4]. While it is now relatively easy to select for monogenic traits, current genome sequence datasets have not been sufficiently mined for more genetically complex (multigenic) performance characteristics, at least in part because of the lack of crop phenotypic information collected from real-world field situations. Furthermore, traditional crop growth analysis often involves destructive sampling that is time-consuming and prone to measurement error. At High-Throughput Estimation of Crop Traits A review of ground and aerial phenotyping platforms

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Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

Section: Crop Yield Estimationmentioning

confidence: 99%

See 1 more Smart Citation

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Jin

Zarco‐Tejada

Schmidhalter

et al. 2021

IEEE Geosci. Remote Sens. Mag.

177

View full text Add to dashboard Cite

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“…Modern tools that use ICTS have enabled remote data collection for agricultural purposes, which has gradually replaced manual methods of collecting pertinent data (Schwalbert et al, 2020). Examples of the modern tools include meters and sensors which are used to regulate water distribution systems such as flow and water quality (Ahmed et al, 2014).…”

Section: Use Of Icts In Agriculturementioning

confidence: 99%

Use of ICTS for socio-economic development of marginalised communities in rural areas: Proposals for establishment of sectoral Rural Entrepreneurial Networks

Nyika¹

2020

J. Dev and Commun Stud

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Information, Communication, Technology and Services (ICTS) is increasingly being used in various fields that include agriculture, education, medicine, tourism and business. However, due to challenges caused by the digital divide and other factors, the use of ICTS and its contribution to socio-economic development is generally more intense in developed countries than in developing countries, and also more in urban areas than in rural areas. This article gives an overview of ICTS, the extent of its adoption in different fields, factors that hinder ICTS driven socio-economic development in developing countries and rural areas, and various possible ways of maximizing potential benefits that marginalised communities can derive from the limited ICTS that is accessible to them. Keywords: ICTS, development, marginalised, rural, networks

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“…Currently, precision agriculture has been used to monitor the crop throughout the cultivation. Precision agriculture is a set of techniques that allows for the specific management of crops and the optimization of production expenses by knowing the variability of different factors, such as soil fertility, pest and disease incidence, and the physiological condition of the plant (XU;SU, 2017;GAO et al, 2018;SCHWALBERT et al, 2020). One of the main techniques of precision agriculture is remote sensing (RS), a non-destructive and accurate method for monitoring and characterizing areas or objects.…”

Section: Introductionmentioning

confidence: 99%

Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

et al. 2020

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Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

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Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

Cited by 258 publications

References 29 publications

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Use of ICTS for socio-economic development of marginalised communities in rural areas: Proposals for establishment of sectoral Rural Entrepreneurial Networks

Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

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