Ratoon rice strategy for enhanced water resource management: A simulation-based study in tropical monsoon climates DOI Creative Commons
Shutaro Shiraki,

Kywae,

Nwe Ni

и другие.

Agricultural Water Management, Год журнала: 2024, Номер 307, С. 109251 - 109251

Опубликована: Дек. 21, 2024

Язык: Английский

Oilpalm-RTMDet: An lightweight oil palm detector base on RTMDet DOI Creative Commons

Jirong Ding,

Runlian Huang,

Yehua Liang

и другие.

Ecological Informatics, Год журнала: 2025, Номер 85, С. 103000 - 103000

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

3
Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia DOI Creative Commons
Abigail Birago Adomako, Ehsan Jolous Jamshidi, Yusri Yusup

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102898 - 102898

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

6
Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles DOI Creative Commons
Gabriel Ítalo Novaes da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Wagner Martins dos Santos

и другие.

Agriculture, Год журнала: 2024, Номер 14(12), С. 2166 - 2166

Опубликована: Ноя. 28, 2024

The objective of this study was to correlate the biophysical parameters forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them machine learning in different agricultural systems. Four experimental units were conducted. Units I II had plant spacings (0.10, 0.20, 0.30, 0.40, 0.50 m) East–West North–South planting directions, respectively. Unit III row (1.00, 1.25, 1.50, 1.75 m), IV cutting frequencies (6, 9, 12 + 6, 18 months) clones “Orelha de Elefante Mexicana”, “Miúda”, “IPA Sertânia”. Plant height width, cladode area index, fresh dry matter yield (FM DM), content, fifteen range analyzed. RGBVI ExGR stood out for presenting greater correlations FM DM. prediction analysis using Random Forest algorithm, highlighting DM, which presented a mean absolute error 1.39, 0.99, 1.72 Mg ha−1 II, III, IV, results showed potential application RGB images predictive cactus.

Язык: Английский

Процитировано

3
The usage of machine learning approach in predicting oil extraction rate based on yield making, yield taking and mill processing factors on oil palm plantations in East Kalimantan, Indonesia DOI Open Access

NA Suharyanti,

H. Heriansyah,

Ernawati Sinaga

и другие.

IOP Conference Series Earth and Environmental Science, Год журнала: 2025, Номер 1477(1), С. 012055 - 012055

Опубликована: Апрель 1, 2025

Abstract Using Machine Learning to implement Precision Agriculture in oil palm plantations has shown its essential role, especially helping with data analysis and facilitating decision-making. This paper aims build an Oil Extraction Rate (OER) prediction model based on Yield Making, Taking, Mill Processing factors. Making variables are parthenocarpy, rainfall, average bunch weight. Taking harvesting interval, ripe, unripe, over-ripe, empty bunch, rotten, loose fruit. CPO production, nucleus OER, plasma FFB, smallholders mill throughput, losses, unstriped bunches, processing hours. study was carried out at the private plantation PT. Triputra Agro Persada Tbk East Kalimantan, Indonesia. We employ monthly time series from 2020 - 2022 as training 2023 testing data. research examined compared performance of Multiple Linear Regression (MLR), Random Forest (RF), Support Vector (SVR), Gradient Boosting (GB) algorithms. The best modeling obtained using algorithm MAPE R 2 values 0.075 (92.5% accuracy) 0.86.

Язык: Английский

Процитировано

0
Modelling height to crown base using non-parametric methods for mixed forests in China DOI Creative Commons
Zeyu Zhou, Huiru Zhang, Ram P. Sharma

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102957 - 102957

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
The quality of university educational programs on environmental and agrobiological focus in graduates’ opinion DOI Creative Commons
Valentina Ivashova, Л. К. Парсиева,

Juliya Lesnykh

и другие.

BIO Web of Conferences, Год журнала: 2024, Номер 130, С. 08009 - 08009

Опубликована: Янв. 1, 2024

The article presents the opinions of universities’ graduates in South Russia on quality university educational programs environmental and agrobiological focus. A brief review publications conducted basis systematic selection main knowledge-intensive databases confirms relevance proposed research question. assessment training specialists is considered by scientific community as a significant social problem. empirical part study examines terms their assessments education received. total 396 took survey. results were processed SPSS Statistics program (version 24). strategic vectors increasing field ecology agrobiology are: development competencies for environmentally safe responsible entrepreneurship; international standardization agricultural to ensure sustainable production, food safety; work with digital process assistants.

Язык: Английский

Процитировано

0
U + LSTM-F: A data-driven growth process model of rice seedlings DOI Creative Commons
Xin Tian,

Wan-Quan Cao,

Shaowen Liu

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102922 - 102922

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0
Ratoon rice strategy for enhanced water resource management: A simulation-based study in tropical monsoon climates DOI Creative Commons
Shutaro Shiraki,

Kywae,

Nwe Ni

и другие.

Agricultural Water Management, Год журнала: 2024, Номер 307, С. 109251 - 109251

Опубликована: Дек. 21, 2024

Язык: Английский

Процитировано

0