A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints

Agriculture, Год журнала: 2024, Номер 14(7), С. 1141 - 1141

Опубликована: Июль 14, 2024

Water is considered one of the vital natural resources and factors for performing short- long-term agricultural practices on Earth. Meanwhile, globally, most available freshwater are utilized irrigation purposes in agriculture. Currently, many world regions facing extreme water shortage problems, which can worsen if not managed properly. In literature, numerous methods remedies used to cope with increasing global crises. The use precision water-saving systems (PISs) efficient management under climate change them a highly recommended approach by researchers. It mitigate adverse effects changing help enhance efficiency, crop yield, environmental footprints. Thus, present study aimed comprehensively examine review PISs, focusing their development, implementation, positive impacts sustainable management. addition, we searched literature using different online search engines reviewed summarized main results previously published papers PISs. We discussed traditional method its modernization enhancing PIS monitoring controlling, architecture, data sharing communication technologies, role artificial intelligence water-saving, future prospects PIS. Based brief review, concluded that PISs seems bright, driven need systems, technological advancements, awareness. As scarcity problem intensifies due population growth, poised play critical optimizing modernizing usage, reducing footprints, thus ensuring agriculture development.

Язык: Английский

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Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Journal of Hydrology, Год журнала: 2024, Номер 637, С. 131336 - 131336

Опубликована: Май 12, 2024

Язык: Английский

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Estimation of soil moisture in drip-irrigated citrus orchards using multi-modal UAV remote sensing

Agricultural Water Management, Год журнала: 2024, Номер 302, С. 108972 - 108972

Опубликована: Июль 30, 2024

Accurate and timely prediction of soil moisture in orchards is crucial for making informed irrigation decisions at a regional scale. Conventional methods monitoring are often limited by high cost disruption structure, etc. However, unmanned aerial vehicle (UAV) remote sensing, with spatial temporal resolutions, offers an effective alternative moisture. In this study, multi-modal UAV sensing data, including RGB, thermal infrared (TIR), multi-spectral (Mul) were acquired citrus orchards. The correlations between different sensor data analyzed to construct seven input combinations. Convolutional neural network (CNN), long short-term memory (LSTM) models new hybrid model (CNN-LSTM), employed predict depths 5 cm, 10 20 cm 40 cm. Additionally, the impact standalone sensor, texture features multi-sensor fusion on accuracy was explored. results indicated that RGB + Mul TIR achieved highest accuracy, followed those Mul, coefficient determination (R2) ranging 0.80–0.88, 0.64–0.84, 0.60–0.81, root mean square error (RMSE) 2.46–2.99 m3·m−3, 2.86–3.89 m3·m−3 3.15–4.25 respectively. Among single inputs, has 0.54–0.72, 0.36–0.52 0.14–0.26, 3.72–4.58 %, 3.81–5.04 % 4.27–6.21 CNN-LSTM exhibited CNN LSTM models, 0.20–0.88, 0.16–0.83, 0.14–0.81, 2.46–5.01 2.68–5.35 2.81–6.21 depth average 0.63, 0.62, 0.59, 0.55, 3.70 3.79 3.85 4.21 Therefore, recommended orchard. It provides method support precision decision-making.

Язык: Английский

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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features

Sensors, Год журнала: 2025, Номер 25(3), С. 618 - 618

Опубликована: Янв. 21, 2025

Chlorophyll is crucial for pear tree growth and fruit quality. In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices textural features realize estimation of SPAD value leaves, this study used UAV remote sensing images ground measurements extract features, analyze their correlation with leaves during expansion period tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), optimized integration algorithm (OIA), were construct inversion models trees, different feature inputs based on indices, combinations, respectively. Moreover, differences among these compared. The results showed following: (1) both significantly correlated values, which important indicators estimating values leaves; (2) combining improved accuracy compared a single type; (3) algorithms demonstrated good predictive ability, OIA model outperformed model, having best accuracy, R2 0.931 0.877 training validation sets, This efficacy integrating multiple accurately invert which, in turn, supported refined management orchards.

Язык: Английский

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Deep learning in multi-sensor agriculture and crop management

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 335 - 379

Опубликована: Янв. 1, 2025

Язык: Английский

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Soil moisture prediction using a hybrid meta-model based on random forest and multilayer perceptron algorithm

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(4)

Опубликована: Март 19, 2025

Язык: Английский

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Characterizing the antecedent rainfall and ATI-MODIS-derived soil moisture content of shallow landslides in Taiwan

Landslides, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

Язык: Английский

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Soil moisture retrieval and trend prediction using multi-temporal remote sensing data: An interpretable deep regression approach

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128172 - 128172

Опубликована: Май 1, 2025

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Soil Moisture Content Inversion Model on the Basis of Sentinel Multispectral and Radar Satellite Remote Sensing Data

Journal of soil science and plant nutrition, Год журнала: 2024, Номер unknown

Опубликована: Окт. 25, 2024

Язык: Английский

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A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning

Agricultural Water Management, Год журнала: 2024, Номер 306, С. 109173 - 109173

Опубликована: Ноя. 22, 2024

Язык: Английский

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