Multi-Genotype Rice Yield Prediction Based on Time-Series Remote Sensing Images and Dynamic Process Clustering DOI Creative Commons
Qian Li,

Shaoshuai Zhao,

Lei Du

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 15(1), P. 64 - 64

Published: Dec. 29, 2024

Predicting rice yield in a timely, precise, and efficient manner is crucial for directing agricultural output creating food policy. The goal of this work was to create stable, high-precision estimate model the prediction multi-genotype combined with dynamic growth processes. By obtaining RGB multispectral data canopy during whole development stage, several bands reflectance, vegetation index, height, volume were retrieved. These remote sensing properties used define curves rice-growing process. k-shape technique utilized cluster various characteristics based on features, from different groups subsequently employed estimation model. results demonstrated that, comparison utilizing solely spectral geometric factors, accuracy process clustering much higher. With root mean square error 315.39 kg/ha coefficient determination 0.82, calculation temporal most accurate. proposed approach can support precision agriculture improve extraction related

Language: Английский

Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming DOI Creative Commons

Sujith Makam,

Bharath Kumar Komatineni,

Sanwal Singh Meena

et al.

Discover Internet of Things, Journal Year: 2024, Volume and Issue: 4(1)

Published: Sept. 9, 2024

Language: Английский

Citations

10
Enhancing the accuracy of monitoring effective tiller counts of wheat using multi-source data and machine learning derived from consumer drones DOI

Ziheng Feng,

Jiaxiang Cai, Ke Wu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110120 - 110120

Published: Feb. 24, 2025

Language: Английский

Citations

1
Status and Development Prospects of Solar-Powered Unmanned Aerial Vehicles—A Literature Review DOI Creative Commons
Krzysztof Sornek,

Joanna Augustyn-Nadzieja,

Izabella Rosikoń

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(8), P. 1924 - 1924

Published: April 10, 2025

Solar-powered unmanned aerial vehicles are fixed-wing aircraft designed to operate solely on solar power. Their defining feature is an advanced power system that uses cells absorb sunlight during the day and convert it into electrical energy. Excess energy generated flight can be stored in batteries, ensuring uninterrupted operation night. By harnessing of sun, these offer key benefits such as extended endurance, reduced dependence fossil fuels, cost efficiency improvements. As a result, they have attracted considerable attention variety military civil applications, including surveillance, environmental monitoring, agriculture, communications, weather fire detection. This review presents selected aspects development use solar-powered aircraft. First, general classification presented. Then, design process discussed, issues structure materials used aircraft, integration wings, selection appropriate battery technologies, optimization management ensure their efficient reliable operation. General information above areas supplemented by presentation results discussed literature sources. Finally, practical applications with examples wildfire The work summarized via discussion future research directions for intended motivate further focusing widespread clean, efficient, environmentally friendly various applications.

Language: Английский

Citations

1
Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach DOI Creative Commons
Marcelo Rodrigues Barbosa Júnior,

Lucas de Azevedo Sales,

Regimar Garcia dos Santos

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100808 - 100808

Published: Jan. 1, 2025

Language: Английский

Citations

0
Algorithms for Plant Monitoring Applications: A Comprehensive Review DOI Creative Commons
Giovanni Paolo Colucci, Paola Battilani, Marco Camardo Leggieri

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 84 - 84

Published: Feb. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Language: Английский

Citations

0
Metabolome Profiling and Predictive Modeling of Dark Green Leaf Trait in Bunching Onion Varieties DOI Creative Commons

Tetsuya Nakajima,

Mari Kobayashi,

Masato Fuji

et al.

Metabolites, Journal Year: 2025, Volume and Issue: 15(4), P. 226 - 226

Published: March 26, 2025

Background: The dark green coloration of bunching onion leaf blades is a key determinant market value, nutritional quality, and visual appeal. This trait regulated by complex network pigment interactions, which not only determine but also serve as critical indicators plant growth dynamics stress responses. study aimed to elucidate the mechanisms regulating develop predictive model for accurately assessing composition. These advancements enable efficient selection varieties facilitate establishment optimal environments through monitoring. Methods: Seven lines heat-tolerant onions were analyzed, including two commercial F1 cultivars, along with purebred three hybrid bred in Yamaguchi Prefecture. analysis was conducted on visible spectral reflectance data (400–700 nm at 20 intervals) compounds (chlorophyll a, chlorophyll b pheophytin lutein, β-carotene), whereas primary secondary metabolites assessed using widely targeted metabolomics. In addition, random forest regression constructed compound contents. Results: Principal component based comparative profiling 186 revealed characteristic metabolite accumulation associated each color pattern. “green” group showed greater sugars, “gray green” characterized phenolic compounds, “dark exhibited cyanidins. are suggested accumulate response environmental stress, these differences likely influence traits. Furthermore, among models estimating contents, one content achieved high accuracy, an R2 value 0.88 test dataset 0.78 Leave-One-Out Cross-Validation, demonstrating its potential practical application evaluation. However, since developed this obtained from greenhouse conditions, it necessary incorporate field trial results reconstruct enhance adaptability. Conclusions: that cyanidin involved characteristics varieties. Additionally, demonstrated can be predicted reflectance. findings suggest developing markers trait, selecting high-pigment-accumulating varieties, facilitating simple real-time diagnosis conditions status, thereby enabling conditions. Future studies will aim genetic factors accumulation, breeding enhanced traits summer cultivation.

Language: Английский

Citations

0
Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China DOI Creative Commons

Jiaxiang Zhai,

Nan Wang, Bifeng Hu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3671 - 3671

Published: Oct. 1, 2024

Texture features have been consistently overlooked in digital soil mapping, especially salinization mapping. This study aims to clarify how leverage texture information for monitoring through remote sensing techniques. We propose a novel method estimating salinity content (SSC) that combines spectral and from unmanned aerial vehicle (UAV) images. Reflectance, index, one-dimensional (OD) were extracted UAV Building on the features, we constructed two-dimensional (TD) three-dimensional (THD) indices. The technique of Recursive Feature Elimination (RFE) was used feature selection. Models estimation built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), Convolutional Neural Network (CNN). Spatial distribution maps then generated each model. effectiveness proposed is confirmed utilization 240 surface samples gathered an arid region northwest China, specifically Xinjiang, characterized by sparse vegetation. Among all indices, TDTeI1 has highest correlation with SSC (|r| = 0.86). After adding multidimensional information, R2 RF model increased 0.76 0.90, improvement 18%. models, outperforms PLSR CNN. model, which (SOTT), achieves RMSE 5.13 g kg−1, RPD 3.12. contributes 44.8% prediction, contributions TD THD indices 19.3% 20.2%, respectively. confirms great potential introducing semi-arid regions.

Language: Английский

Citations

2
Subtropical region tea tree LAI estimation integrating vegetation indices and texture features derived from UAV multispectral images DOI Creative Commons

Zhong-Han Zhuang,

Hui-Ping Tsai, Chung-I Chen

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100650 - 100650

Published: Nov. 10, 2024

Language: Английский

Citations

1
Multi-Genotype Rice Yield Prediction Based on Time-Series Remote Sensing Images and Dynamic Process Clustering DOI Creative Commons
Qian Li,

Shaoshuai Zhao,

Lei Du

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 15(1), P. 64 - 64

Published: Dec. 29, 2024

Predicting rice yield in a timely, precise, and efficient manner is crucial for directing agricultural output creating food policy. The goal of this work was to create stable, high-precision estimate model the prediction multi-genotype combined with dynamic growth processes. By obtaining RGB multispectral data canopy during whole development stage, several bands reflectance, vegetation index, height, volume were retrieved. These remote sensing properties used define curves rice-growing process. k-shape technique utilized cluster various characteristics based on features, from different groups subsequently employed estimation model. results demonstrated that, comparison utilizing solely spectral geometric factors, accuracy process clustering much higher. With root mean square error 315.39 kg/ha coefficient determination 0.82, calculation temporal most accurate. proposed approach can support precision agriculture improve extraction related

Language: Английский

Citations

0