Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence

Carneiro, Franciele Morlin; Oliveira, Maílson Freire de; Almeida, Samira Luns Hatum de; Filho, Armando Lopes de Brito; Furlani, Carlos Eduardo Angeli; Rolim, Glauco de Souza; Ferraudo, Antônio Sérgio; Silva, Rouverson Pereira da

doi:10.14393/bj-v38n0a2022-55925

Cited by 3 publications

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“…Many authors used VI to estimate the biomass. In pasture, seven VI, used to estimate the biomass, and the NDVI showed a good relationship described by the accuracy and precision of the models [13], the soybean biomass estimate showed a strong correlation with the VI in Brazil [14], and in corn with the use of VI it was possible to estimate the biomass 77 days after sowing of the irrigated plants [15].…”

Section: Introductionmentioning

confidence: 94%

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

et al. 2023

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Remote sensing technology applied to agricultural crops has emerged as an efficient tool to speed up the data acquisition process in decision-making. In this study, we aimed to evaluate the performance of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE) in estimating biomass accumulation in common bean crops. The research was conducted at the Federal University of Lavras, where the ANFC 9 cultivar was used in an area of approximately seven hectares, in a second crop, in 2022. A total of 31 georeferenced points spaced at 50 m were chosen to evaluate height, width and green biomass, with collections on days 15, 27, 36, 58, 62 and 76 of the crop cycle. The images used in the study were obtained from the PlanetScope CubeSat satellite, with a spatial resolution of 3 m. The data obtained were subjected to a Pearson correlation (R) test and multiple linear regression analysis. The green biomass variable was significantly correlated with plant height and width. The NDVI performed better than the NDRE, with higher values observed at 62 Days After Sowing (DAS). The model that integrates the parameters of height, width and NDVI was the one that presented the best estimate for green biomass in the common bean crop. The M1 model showed the best performance to estimate green biomass during the initial stage of the crop, at 15, 27 and 36 DAS (R2 = 0.93). These results suggest that remote sensing technology can be effectively applied to assess biomass accumulation in common bean crops and provide accurate data for decision-makers.

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Section: Introductionmentioning

confidence: 94%

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

et al. 2023

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AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery

Canata,

Júnior,

de Oliveira

et al. 2024

Sugar Tech

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Artificial Intelligence in Agricultural Mapping: A Review

Espinel,

Herrera-Franco,

Rivadeneira García

et al. 2024

Agriculture

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Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements.

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Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence

Cited by 3 publications

References 0 publications

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery

Artificial Intelligence in Agricultural Mapping: A Review

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