Unveiling Temporal and Spatial Research Trends in Precision Agriculture: A BERTopic Text Mining Approach DOI Creative Commons
Yang Liu, Fanghao Wan

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e36808 - e36808

Published: Aug. 24, 2024

This study leverages the BERTopic algorithm to analyze evolution of research within precision agriculture, identifying 37 distinct topics categorized into eight subfields: Data Analysis, IoT, UAVs, Soil and Water Management, Crop Pest Livestock, Sustainable Agriculture, Technology Innovation. By employing BERTopic, based on a transformer architecture, this enhances topic refinement diversity, distinguishing it from traditional reviews. The findings highlight significant shift towards IoT innovations, such as security privacy, reflecting integration smart technologies with agricultural practices. Notably, introduces comprehensive popularity index that integrates trend intensity proportion, providing nuanced insights dynamics across countries journals. analysis shows regions robust development, USA Germany, are advancing in like Machine Learning while diversity topics, assessed through information entropy, indicates varied global scope. These assist scholars institutions selecting directions provide newcomers an understanding field's dynamics.

Language: Английский

Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review DOI Creative Commons
Sewnet Getahun, Habtamu Kefale, Yohannes Gelaye

et al.

The Scientific World JOURNAL, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Precision agriculture technologies (PATs) transform crop production by enabling more sustainable and efficient agricultural practices. These utilize data-driven approaches to optimize the management of crops, soil, resources, thus enhancing both productivity environmental sustainability. This article reviewed application PATs for sustainability around globe. Key components PAT include remote sensing, GPS-guided equipment, variable rate technology (VRT), Internet Things (IoT) devices. Remote sensing drones deliver high-resolution imagery data, precise monitoring health, soil conditions, pest activity. machinery ensures accurate planting, fertilizing, harvesting, which reduces waste enhances efficiency. VRT optimizes resource use allowing farmers apply inputs such as water, fertilizers, pesticides at varying rates across a field based on real-time data specific requirements. over-application minimizes impact, nutrient runoff greenhouse gas emissions. IoT devices sensors provide continuous conditions status, timely informed decision-making. The contributes significantly promoting practices that conserve reduce chemical usage, enhance health. By precision operations, these impact farming, while simultaneously boosting yields profitability. As global demand food increases, offers promising pathway achieving ensuring long-term

Language: Английский

Citations

28
Factors Influencing Precision Agriculture Technology Adoption Among Small-Scale Farmers in Kentucky and Their Implications for Policy and Practice DOI Creative Commons
S. C. Pāṇḍeya, Buddhi Gyawali, Suraj Upadhaya

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 177 - 177

Published: Jan. 15, 2025

The increasing pressure on food security and environmental sustainability has emphasized the importance of effective farm resource usage. Precision agriculture technologies (PATs) have been considered as one solutions to these challenges. Multiple stakeholders agencies working in sector implemented various initiatives facilitate their adoption. Despite numerous initiatives, adoption PATs small farms is shallow United States. It important understand what socio-economic demographic factors influence decision-making regarding PAT This research aimed provide actionable insights that can help farmers overcome existing challenges capitalize benefits advanced agricultural practices, ultimately contributing resilience sector. study used a mixed approach (a combination mail, in-person, focus group discussion) investigate influencing by small-scale Kentucky. data were analyzed using binary logistic regression method. results revealed size longer years farming experience increased likelihood adoption, whereas farmers’ age negatively affected Other variables, such gender, income, education, did not significantly. To promote among Kentucky, policies should supporting younger building suitable for operating reducing barriers. Furthermore, providing targeted training resources technologies, thereby improving efficiency sustainability.

Language: Английский

Citations

2
Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry DOI Creative Commons
Md. Toukir Ahmed,

Md Wadud Ahmed,

Ocean Monjur

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100533 - 100533

Published: Aug. 8, 2024

As the demand for food surges and agricultural sector undergoes a transformative shift towards sustainability efficiency, need precise proactive measures to ensure health welfare of livestock becomes paramount. In egg hatchery industry, hyperspectral imaging (HSI) has emerged as cutting-edge, non-destructive technique fast accurate quality analysis, including detecting chick embryo mortality. However, high cost operational complexity compared conventional RGB are significant bottlenecks in widespread adoption HSI technology. To overcome these hurdles unlock full potential HSI, promising solution is image reconstruction from standard images. This study aims reconstruct images early prediction Initially, performance different algorithms, such HRNET, MST++, Restormer, EDSR were eggs incubation period. Later, reconstructed spectra used differentiate live dead embryos using XGBoost Random Forest classification methods. Among methods, HRNET showed impressive with MRAE 0.0955, RMSE 0.0159, PSNR 36.79 dB. motivated idea that harnessing technology integrated smart sensors data analytics improve automation, enhance biosecurity, optimize resource management sustainable agriculture 4.0.

Language: Английский

Citations

12
Tree Crop Yield Estimation and Prediction Using Remote Sensing and Machine Learning: A Systematic Review DOI Creative Commons

Carolina Trentin,

Yiannis Ampatzidis, Christian Lacerda

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100556 - 100556

Published: Sept. 1, 2024

Yield prediction has long been a valuable tool for farmers seeking to enhance crop production. Among the many ways predict yield, integration of machine learning (ML) techniques is becoming more common refining methodologies. This study highlights current landscape remote sensing and ML employed in predicting tree yield while also identifying critical gaps areas further exploration. Studies with limited datasets training often use simpler models such as linear regression, studies larger complex models, including deep learning, ensemble methods, hyperparameter tuning; these cases, performance evaluation tends be sophisticated. using demonstrated accuracy levels ranging from 50% 99%. smaller consistently demonstrate higher rates. While can prediction, their effectiveness depends on strategic data collection multi-factor multi-method approach. Integration various sources, weather, soil, plant data, could model resilience applicability. Enhancing research this field achieved through overcoming challenges accurate fostering development open datasets. comprehensive analysis lays groundwork future endeavors aimed at advancing application accurately yield.

Language: Английский

Citations

10
Unlocking China's grain yield potential: Harnessing technological and spatial synergies in diverse cropping systems DOI
Zhenzhong Dai,

Sen Chang,

Guorong Zhao

et al.

Agricultural Systems, Journal Year: 2025, Volume and Issue: 226, P. 104308 - 104308

Published: March 9, 2025

Language: Английский

Citations

1
Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions DOI

Yiguang Fan,

Haikuan Feng, Yang Liu

et al.

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 161, P. 127388 - 127388

Published: Oct. 18, 2024

Language: Английский

Citations

5
Crop Substrates for Sustainable Hydroponic Farming DOI Creative Commons
Tesfahun Belay Mihrete

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

Hydroponic farming, as a method of cultivating plants in nutrient-rich water solutions without soil, presents compelling solution to contemporary food security challenges. This chapter explores the pivotal role crop substrates sustainable hydroponic systems, emphasizing their functions supporting plant growth and impact on resource efficiency environmental sustainability. I discuss various types substrates, including inert materials like perlite organic alternatives such coconut coir, focusing unique properties contributions nutrient management, root health, retention. The highlights challenges substrate degradation pH alongside opportunities for innovation technology regulatory frameworks. It concludes by advocating integration best practices technological advancements optimize farming enhanced sustainability, productivity, resilience agriculture.

Language: Английский

Citations

0
Lightweight GAN-Assisted Class Imbalance Mitigation for Apple Flower Bud Detection DOI Creative Commons
Wenan Yuan, Peng Li

Big Data and Cognitive Computing, Journal Year: 2025, Volume and Issue: 9(2), P. 28 - 28

Published: Jan. 29, 2025

Multi-class object detectors often suffer from the class imbalance issue, where substantial model performance discrepancies exist between classes. Generative adversarial networks (GANs), an emerging deep learning research topic, are able to learn existing data distributions and generate similar synthetic data, which might serve as valid training for improving detectors. The current study investigated utility of lightweight unconditional GAN in addressing weak detector by incorporating into real retraining, under agricultural context. AriAplBud, a multi-growth stage aerial apple flower bud dataset was deployed study. A baseline YOLO11n first developed based on training, validation, test datasets derived AriAplBud. Six FastGAN models were dedicated subsets same YOLO validation different growth stages. Positive sample rates average instance number per image generated each 1000 images at various confidence thresholds. In total, 13 new retrained specifically two stages, tip half-inch green, including increase total 1000, 2000, 4000, 8000, respectively, pseudo-labeled detector. showed its resilience successfully generating positive samples, despite instances being generally small randomly distributed images. negatively correlated with thresholds expected, ranged 0 1. Higher overall observed stages higher performance. contained fewer detector-detectable than corresponding best achieved AP improvements green 30.13% 14.02% while mAP improvement 2.83%. However, relationship quantity performances had yet be determined. concluded beneficial retraining their performances. Further studies still need investigate influence quality

Language: Английский

Citations

0
A Data Framework for Monitoring Bioeconomy Transition: A Combined PDSA Methodology and DSS Approach DOI

Benjamas Kumsueb,

Chitnucha Buddhaboon,

Bounthanh Keobualapha

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Plants breathing under pressure: mechanistic insights into soil compaction-induced physiological, molecular and biochemical responses in plants DOI
Md. Mahadi Hasan, Xu‐Dong Liu, Md Atikur Rahman

et al.

Planta, Journal Year: 2025, Volume and Issue: 261(3)

Published: Feb. 2, 2025

Language: Английский

Citations

0