Sorghum cultivation plays a pivotal role in addressing food insecurity in South Sudan, but persistent conflict continues to impose challenges in the agriculture sector therefore understanding the impact of conflict on sorghum yield prediction is impo...
Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ETo). Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single ...
Environmental monitoring and assessment
Feb 4, 2025
Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning id...
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Feb 2, 2025
There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, and supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. ...
The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water management in crop fields. This study aims to enhance the forecasting of evapotranspiration (ET), potential e...
Biochar, a widely utilized soil amendment in environmental applications, has been employed to enhance tea cultivation. This study utilized three machine learning models to investigate the effects of biochar on tea growth and yield, with the random fo...
Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, t...
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this stud...
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural...
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and y...
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