Deep Learning for Sustainable Agriculture: A Systematic Review on Applications in Lettuce Cultivation

Sustainability, Год журнала: 2025, Номер 17(7), С. 3190 - 3190

Опубликована: Апрель 3, 2025

Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, core technology smart agriculture, has revolutionized the lettuce industry through powerful computer vision techniques like convolutional neural networks (CNNs) and YOLO-based models. This review systematically examines deep learning applications including pest disease diagnosis, precision spraying, pesticide residue detection, crop condition monitoring, growth stage classification, yield prediction, weed management, irrigation fertilization management. Notwithstanding significant contributions, several critical challenges persist, constrained model generalizability dynamic settings, exorbitant computational requirements, paucity of meticulously annotated datasets. Addressing these is essential improving efficiency, adaptability, sustainability learning-driven solutions production. By enhancing resource reducing chemical inputs, optimizing cultivation practices, contributes to broader goal explores research progress, optimization strategies, future directions strengthen learning’s role fostering farming.

Язык: Английский

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AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture

SoftwareX, Год журнала: 2025, Номер 30, С. 102083 - 102083

Опубликована: Фев. 11, 2025

Язык: Английский

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Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development

Sustainability, Год журнала: 2025, Номер 17(5), С. 2250 - 2250

Опубликована: Март 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Язык: Английский

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Research progress of non-destructive testing techniques in moisture content determination

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100878 - 100878

Опубликована: Март 1, 2025

Язык: Английский

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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model

Agronomy, Год журнала: 2025, Номер 15(3), С. 696 - 696

Опубликована: Март 13, 2025

Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility soil heterogeneous necessitate use of numerical simulations their effective regional-scale applications. The existing simulation methods like physical process models machine learning (ML) algorithms have limitations: struggle with parameter acquisition at regional scales, ML face difficulties settings due presence crops. As more advanced complex branch ML, deep even greater limitations related crop growth management. To address these challenges, this study proposed novel hybrid system that merged model. Initially, we employed Random Forest (RF) regression model integrated multi-source environmental factors estimate prior sowing wheat, achieving an average coefficient determination (R2) 0.8618, root mean square error (RMSE) 0.0182 m3 m−3, absolute (MAE) 0.0148 m−3 across eight depths. RF provided vital parameters operation Water Balance Winter Wheat (WBWW) scale, enabling assessments combined Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between system-generated results actual disaster records during two events highlighted its efficacy. Finally, utilized examine spatiotemporal variations patterns HHH region over past decades. findings revealed overall intensification conditions decline SMAPI rate −0.021% per year. Concurrently, there has been shift patterns, characterized increase both frequency extremity events, duration intensity individual decreased majority Additionally, identified northeastern, western, southern areas requiring concentrated attention targeted intervention strategies. These efforts signify notable application fusion techniques integration within big context, thereby facilitating prevention, management, mitigation

Язык: Английский

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Improving Soil Moisture Prediction Using Gaussian Process Regression

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100905 - 100905

Опубликована: Март 1, 2025

Язык: Английский

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A backtracking search-based extreme gradient boosting algorithm for soil moisture prediction using meteorological variables

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 16, 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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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China

Agronomy, Год журнала: 2025, Номер 15(3), С. 759 - 759

Опубликована: Март 20, 2025

The rapid and accurate acquisition of soil moisture (SM) information is essential. Although Unmanned Aerial Vehicle (UAV) remote sensing technology has made significant advancements in SM monitoring, existing studies predominantly focus on developing models tailored to specific regions. transferability these across different regions remains a considerable challenge. Therefore, this study proposes transfer learning-based framework, using two representative small agricultural watersheds (Hongxing region Woniutu region) Northeast China as case studies. This framework involves pre-training model source domain fine-tuning it with limited set target samples achieve high-precision inversion. evaluates the performance three algorithms: Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network. Results show that fine-tuned significantly mitigates decline prediction accuracy caused by regional differences. LSTM achieved highest retrieval accuracy, following results: 10% (R = 0.615, RRMSE 15.583%), 30% 0.682, 13.97%), 50% 0.767, 16.321%). Among models, exhibited most improvement best transferability. underscores potential learning for enhancing cross-regional monitoring providing valuable insights future UAV-based monitoring.

Язык: Английский

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High-Resolution Soil Moisture Estimation: A Case Study in Coastal India

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Язык: Английский

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