AIP conference proceedings, Год журнала: 2024, Номер 3193, С. 020279 - 020279
Опубликована: Янв. 1, 2024
Язык: Английский
Agriculture, Год журнала: 2025, Номер 15(4), С. 377 - 377
Опубликована: Фев. 11, 2025
Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision improves agricultural productivity profitability while reducing costs environmental impact. However, ML implementation faces challenges such as managing large volumes adequate infrastructure. Despite significant advances applications sustainable agriculture, there is still a lack deep systematic understanding several areas. Challenges include integrating sources adapting models to local conditions. This research aims identify trends key players associated with use agriculture. A review was conducted using the PRISMA methodology bibliometric analysis capture relevant studies from Scopus Web Science databases. The study analyzed literature between 2007 2025, identifying 124 articles that meet criteria for certainty assessment. findings show quadratic polynomial growth publication on notable increase up 91% per year. most productive years were 2024, 2022, 2023, demonstrating growing interest field. highlights importance multiple improved decision making, soil health monitoring, interaction climate, topography, properties land crop patterns. Furthermore, evolved weather advanced technologies like Internet Things, remote sensing, smart farming. Finally, agenda need deepening expansion predominant concepts, farming, develop more detailed specialized explore new maximize benefits sustainability.
Язык: Английский
Процитировано
6
Geomatics, Год журнала: 2024, Номер 4(2), С. 91 - 123
Опубликована: Март 22, 2024
Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis resource management, geospatial technology, i.e., the use remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology more cost-effective and requires less labor compared ground-based surveys, making it suitable a wide range agricultural applications. Effectively utilizing timely, accurate, objective data provided by RS technologies presents challenge field management. Satellite-based measurements offer consistent information on hydrological conditions across extensive land areas. In this study, we carried out detailed focused addressing issues India through application GIS technologies. Adhering Preferred Reporting Items Systematic Reviews Meta-Analysis (PRISMA) guidelines, systematically reviewed published research articles, providing comprehensive analysis. This study aims explore practices goal enhancing their effectiveness efficiency. primarily examines current Indian sustainability. We revealed that considerable has used multispectral Landsat series data. Cutting-edge like Sentinel, Unmanned Aerial Vehicles (UAVs), hyperspectral have not been fully investigated assessment monitoring resources. Integrating allows monitoring, offering valuable recommendations effective
Язык: Английский
Процитировано
10
Cogent Food & Agriculture, Год журнала: 2025, Номер 11(1)
Опубликована: Янв. 20, 2025
Global food security is seriously threatened by climate change, which calls for creative agricultural solutions. However, little known about how different smart technologies are integrated to enhance security. As a strategic reaction these difficulties, this review investigates the incorporation of remote sensing (RS) as well artificial intelligence (AI) into climate-smart agriculture (CSA). This demonstrates advances can improve resilience, productivity, and sustainability utilizing AI's capacity predictive analytics, crop modelling, precision agriculture, along with RS's strengths in projections, land management, continuous surveillance. Several important tactics were covered, such combining AI RS regulate risks, maximize resource utilization, practice choices. The also discusses issues like policy frameworks, building, accessibility that prevent from being widely adopted. highlights further CSA offers insights they help ensure systems remain secure changing climates.
Язык: Английский
Процитировано
2
Remote Sensing, Год журнала: 2024, Номер 16(6), С. 1003 - 1003
Опубликована: Март 12, 2024
Yield calculation is an important link in modern precision agriculture that effective means to improve breeding efficiency and adjust planting marketing plans. With the continuous progress of artificial intelligence sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, non-destructive calculation, they been favored by a large number researchers. This article reviews research crop-yield remote images visible light images, describes technical characteristics applicable objects different schemes, focuses detailed explanations data acquisition, independent variable screening, algorithm selection, optimization. Common issues are also discussed summarized. Finally, solutions proposed for main problems arisen so far, future directions predicted, with aim achieving more wider popularization image technology.
Язык: Английский
Процитировано
9
Agronomy, Год журнала: 2024, Номер 14(4), С. 719 - 719
Опубликована: Март 29, 2024
Machine learning is a widespread technology that plays crucial role in digitalisation and aims to explore rules patterns large datasets autonomously solve non-linear problems, taking advantage of multiple source data. Due its versatility, machine can be applied agriculture. Better crop management, plant health assessment, early disease detection are some the main challenges facing agricultural sector. Plant phenotyping play key addressing these challenges, especially when combined with techniques. Therefore, this study reviews available scientific literature on applications algorithms specific focus sunflowers. The most common field described emphasise possible uses. Subsequently, overview highlights application three primaries areas: management (i.e., yield prediction, biomass estimation, growth stage monitoring), nutritional status water stress), detection. Finally, we adoption techniques sunflower phenotyping. has been thoroughly investigated. Artificial neural networks stacked models seems best way analyse
Язык: Английский
Процитировано
9
Natural Hazards, Год журнала: 2025, Номер unknown
Опубликована: Март 7, 2025
Язык: Английский
Процитировано
1
Plant Breeding, Год журнала: 2024, Номер unknown
Опубликована: Окт. 20, 2024
ABSTRACT The global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding bolstered with efficient nondestructive trait documentation approaches vital. High‐throughput phenotyping (HTCP) platforms have prominently emerged as mainstream approach for reducing the bottleneck in programmes. HTCP has potential to provide detailed quantitative information large populations under different growth stages across diverse environmental regimes, facilitating accelerated strategies. New imaging also enable characterization wide range above below‐ground parameters. specificity use sensors, automation data collection, large‐scale handling systems accurate analytical tools substantial role dynamic monitoring big interpretation. are capable making measurements physiological, morphological, biochemical stress responses plants. Developments sensors improved precision, intervention unmanned aerial vehicles, robotics, computed tomography machine learning given dramatic developmental leap phenotyping. This review provides an avenue understanding various high‐throughput platforms, working principles, current developments contributions crops laboratory field conditions. A comparative idea advantages pitfalls these available help researchers choosing right technology suiting specific practical requirements. Furthermore, aims novel future prospects requirements that potentially widen application utilization technologies agriculture.
Язык: Английский
Процитировано
6
e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2024, Номер 8, С. 100611 - 100611
Опубликована: Май 19, 2024
Crop yield prediction (CYP) at the field level is crucial in quantitative and economic assessment for creating agricultural commodities plans import-export strategies enhancing farmer incomes. breeding has always required a significant amount of time money. CYP developed to forecast higher crop production. This paper proposes an efficient deep learning (DL) dimensionality reduction (DR) approaches Indian regional crops. comprised '3' phases: preprocessing, DR, classification. Initially, data south region are collected from dataset. Then preprocessing applied dataset by performing cleaning normalization. After that, DR performed using squared exponential kernel-based principal component analysis (SEKPCA). Finally, based on weight-tuned convolutional neural network (WTDCNN), which predicts high profit. The simulation outcomes shows that proposed method attains superior performance compared exiting schemes with improved accuracy 98.96%. novelty approach lies combination DL, WTDCNN techniques accurate prediction, specifically tailored
Язык: Английский
Процитировано
4
Sustainability, Год журнала: 2024, Номер 16(21), С. 9437 - 9437
Опубликована: Окт. 30, 2024
Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various climatic variables are included in the analysis, encompassing type, year, season, specific conditions Indian state during crop’s growing season. Features such as season were one-hot encoded. The primary objective was predict yield using a deep neural network (DNN), with hyperparameters optimized through genetic algorithms (GAs) maximize R2 score. best-performing model, achieved by fine-tuning its hyperparameters, an 0.92, meaning it explains 92% variation yields, indicating high predictive accuracy. DNN models further analyzed explainable AI (XAI) techniques, specifically local interpretable model-agnostic explanations (LIME), elucidate feature importance enhance model interpretability. analysis underscored significant role features crops, leading incorporation additional dataset classify most optimal crops based on more detailed soil climate data. This classification task also executed GA-optimized DNN, aiming results demonstrate effectiveness this approach predicting classifying crops.
Язык: Английский
Процитировано
3
Current Plant Biology, Год журнала: 2024, Номер unknown, С. 100432 - 100432
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
3