Nitrogen variability assessment of pasture fields under an integrated crop-livestock system using UAV, PlanetScope, and Sentinel-2 data

Pereira, Francisco Rafael da Silva; Lima, Joaquim Pedro de; Freitas, Rodrigo Greggio de; Reis, Aliny Aparecida dos; Amaral, Lucas Rios do; Figueiredo, G. K. D. A.; Lamparelli, Rubens Augusto Camargo; Magalhães, Paulo Sérgio Graziano

doi:10.1016/j.compag.2021.106645

Cited by 21 publications

(13 citation statements)

References 47 publications

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“…The poor performance of the models in predicting dry AGB in tropical pastures has also been observed in previous studies, which reported R 2 values less than 0.30 17,19,32 . The low predictive ability of dry AGB in previous studies 17,19 was attributed to the low variability in the dry biomass dataset used for modeling, whose coe cient of variation was approximately 26% 19 .…”

Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

“…It is noteworthy that the majority of previous studies mentioned above used predictive or machine learning modeling algorithms, such as random forest 27,28, 32 . Machine-learning techniques, such as RF and SVR, could be an asset in detecting the nonlinear relationship between pasture quality and canopy re ectance and circumventing the over tting and multicollinearity problem 32,37 . In this study, the SVR models slightly outperformed the RF models, presumably because SVR has shown better generalization performance when the training datasets are small 31 , as observed in this study.…”

Section: Discussionmentioning

confidence: 99%

“…This is particularly relevant due to the unknown distribution of the data acquired from satellite remote sensing 31 . The main advantages of RF, a well-known regression method, are related to its ability to process high-dimensional data and prevent over tting 32 . In contrast, SVM's main advantages are its robustness to small training datasets and low sensitivity to free parameter settings 31 .…”

Section: Introductionmentioning

confidence: 99%

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Using Sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage biomass, crude protein, and fiber content

Fernandes,

Junior,

Adams

et al. 2023

Preprint

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Grasslands cover approximately 24% of the Earth’s surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and quality of pastures. This study demonstrates the potential of estimating pasture leaf biomass, crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate biomass were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage biomass, crude protein, and fiber content

Fernandes,

Junior,

Adams

et al. 2023

Preprint

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“…Soares proposed a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images [4]. More scholars studied the application of UAV to grassland monitoring, so as to accurately manage the grazing and breeding process and protect the ecology [5][6][7][8][9][10]. Smart grazing and UAV grazing are very popular research topics, which worth attentions and in-depth researches.…”

Section: Introductionmentioning

confidence: 99%

UAV based smart grazing：A prototype and test bed for space-air- ground integrated IoT networks in Qinghai-Tibet plateau

Ji¹,

Ling²,

Huang³

et al. 2023

Preprint

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Smart grazing is a relatively difficult field of digital agriculture. Restricted by the geographical conditions of pastures, poor network infrastructure and low economic output, relevant applications have been difficult to achieve. In this paper, we propose the SAG-GIoT system based on the background of yak grazing production in the Qinghai Tibet Plateau, and define three intelligent grazing management application scenarios: daily grazing supervision, UAV grazing and searching for yaks. To this end, we have designed the three-tier technical architecture of SAG-GIoT, and developed collar, base station and grazing management system. We designed the all-terrain network service scheme with BDS-BSS and SBSs, and verified the daily grazing supervision test in long-term. UAV grazing test was carried out in pasture, and a flexible communication networking was realized through the UAV-BS. With the guidance of UAV searching and APP positioning, taking HBSs in hand, we quickly and accurately find the lost yaks. SAG-GIoT system is characterized as low cost, flexible deployment and global service, and has broad application prospects.

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Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

Wang

Zhu

et al. 2022

Food and Energy Security

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Timely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages.

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Nitrogen variability assessment of pasture fields under an integrated crop-livestock system using UAV, PlanetScope, and Sentinel-2 data

Cited by 21 publications

References 47 publications

Using Sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage biomass, crude protein, and fiber content

Using Sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage biomass, crude protein, and fiber content

UAV based smart grazing：A prototype and test bed for space-air- ground integrated IoT networks in Qinghai-Tibet plateau

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

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