Adoption of Machine Learning Methods for Crop Yield Prediction-based Smart Agriculture and Sustainable Growth of Crop Yield Production – Case Study in Jordan DOI

Moneer Nuser,

Mohammad Alshirah,

Sahar Al Mashaqbeh

et al.

Published: Sept. 5, 2024

Crop yield prediction is significant for global food security and economic systems. Numerous algorithms machine learning have been utilized to support crop due the increasing complexity of factors influencing plant growth. Machine (ML) models are quite tedious because ML agriculture-based complex. This study combines several build a sturdy accurate model. Linear regression predicts measurable response using various predictors assumes linear relation between variable predictors. research explores adoption methods their potential sustainable growth yields. The dataset was collected from two main sources: i) Department Statistics Jordan ii) climate change knowledge portal, which used train proposed model; availability large datasets has cleared path application techniques in prediction. Nine analysis were tested predict yield; more than one algorithm gave very good results XGBoost, multiple regression, Random forest, Lasso give low mean squared errors 0.092, 0.024, 0.023, 0.023. may be remarkably useful algorithms, but there many challenges. One these challenges quality data volume, where need data. Further, intricacy agriculture systems, developing can challenging. In this study, strengths optimization integrated new predictive model developed contributes efficiency production, reducing prices when shortages found. addition, supports process, vital role agricultural planning procedures making decisions. an essential instrument decision assistance prediction, either supporting decisions on suitable grow. algorithm's performance improved by applying innovative techniques. helps policymakers precise forecasts, make evaluations imports exports strengthen nationwide.

Language: Английский

Waste not, want not: unlocking the innovative potential of organic and eco-friendly insect and algal resources for future aquaculture DOI
Sourabh Debbarma,

Suparna Deb,

Nitesh Kumar Yadav

et al.

Aquaculture International, Journal Year: 2025, Volume and Issue: 33(2)

Published: Jan. 22, 2025

Language: Английский

Citations

2
Lactic acid fermentation of non-conventional plant-based protein extract DOI
Saverio Monica,

Elena Bancalari,

Lorenzo Siroli

et al.

Food Research International, Journal Year: 2025, Volume and Issue: 208, P. 116174 - 116174

Published: March 14, 2025

Language: Английский

Citations

1
An introduction to innovative food packaging and processing technologies, the present and the future DOI
Daniela Bermúdez‐Aguirre

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. xxix - lxxx

Published: Jan. 1, 2025

Language: Английский

Citations

0
Wastes valorization to polyhydroxyalkanoate: Key concepts and strategies to overcome potential challenges DOI
Mati Ullah,

Abdul Wahab,

Wajid Hussain

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115779 - 115779

Published: Feb. 1, 2025

Language: Английский

Citations

0
Circular bioeconomy approach for the recovery of polyphenols and dietary fibres from orange pomace waste DOI
N. K. Srivastava, Anupama Kumar,

Shilpshri V. Shinde

et al.

Sustainable Chemistry and Pharmacy, Journal Year: 2025, Volume and Issue: 45, P. 102038 - 102038

Published: May 1, 2025

Language: Английский

Citations

0
Waste to wealth: Polyhydroxyalkanoates (PHA) production from food waste for a sustainable packaging paradigm DOI Creative Commons
Heri Septya Kusuma,

Atna Sabita,

Najla Anira Putri

et al.

Food Chemistry Molecular Sciences, Journal Year: 2024, Volume and Issue: 9, P. 100225 - 100225

Published: Oct. 10, 2024

The growing demand for sustainable food packaging and the increasing concerns regarding environmental pollution have driven interest in biodegradable materials. This paper presents an in-depth review of production Polyhydroxyalkanoates (PHA), a polymer, from waste. PHA-based bioplastics, particularly when derived low-cost carbon sources such as volatile fatty acids (VFAs) waste oils, offer promising solution reducing plastic enhancing sustainability. Through optimization microbial fermentation processes, PHA can achieve significant efficiency improvements, with yields reaching up to 87 % content under ideal conditions. highlights technical advancements using packaging, emphasizing its biodegradability, biocompatibility, potential serve alternative petroleum-based plastics. However, challenges high costs, mechanical limitations, need scalability remain barriers industrial adoption. future hinges on overcoming these through further research innovation techniques, material properties, cost reduction strategies, along necessary legislative support promote widespread use.

Language: Английский

Citations

3
The Challenges and Strategies of Food Security under Global Change DOI Creative Commons
Raquel P. F. Guiné

Foods, Journal Year: 2024, Volume and Issue: 13(13), P. 2083 - 2083

Published: July 1, 2024

Food insecurity corresponds to a deficit in households’ access appropriate food, either quantity and/or quality, due limited financial resources or other factors [...]

Language: Английский

Citations

2
Turning agro-food waste into resources: Exploring the antioxidant effects of bioactive compounds bioaccessibility from digested jabuticaba tree leaf extract DOI
Amanda dos Santos Lima, Thiago Mendanha Cruz, Nima Mohammadi

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 469, P. 142538 - 142538

Published: Dec. 17, 2024

Language: Английский

Citations

1
Adoption of Machine Learning Methods for Crop Yield Prediction-based Smart Agriculture and Sustainable Growth of Crop Yield Production – Case Study in Jordan DOI

Moneer Nuser,

Mohammad Alshirah,

Sahar Al Mashaqbeh

et al.

Published: Sept. 5, 2024

Crop yield prediction is significant for global food security and economic systems. Numerous algorithms machine learning have been utilized to support crop due the increasing complexity of factors influencing plant growth. Machine (ML) models are quite tedious because ML agriculture-based complex. This study combines several build a sturdy accurate model. Linear regression predicts measurable response using various predictors assumes linear relation between variable predictors. research explores adoption methods their potential sustainable growth yields. The dataset was collected from two main sources: i) Department Statistics Jordan ii) climate change knowledge portal, which used train proposed model; availability large datasets has cleared path application techniques in prediction. Nine analysis were tested predict yield; more than one algorithm gave very good results XGBoost, multiple regression, Random forest, Lasso give low mean squared errors 0.092, 0.024, 0.023, 0.023. may be remarkably useful algorithms, but there many challenges. One these challenges quality data volume, where need data. Further, intricacy agriculture systems, developing can challenging. In this study, strengths optimization integrated new predictive model developed contributes efficiency production, reducing prices when shortages found. addition, supports process, vital role agricultural planning procedures making decisions. an essential instrument decision assistance prediction, either supporting decisions on suitable grow. algorithm's performance improved by applying innovative techniques. helps policymakers precise forecasts, make evaluations imports exports strengthen nationwide.

Language: Английский

Citations

0