AIMC Topic: Agriculture
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Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment.
Chemosphere
Nov 25, 2024
Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption condit...
Deployment of intelligent irrigation monitoring system with Android app for machine learning prediction.
Environmental monitoring and assessment
Nov 21, 2024
Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate ...
Towards efficient IoT communication for smart agriculture: A deep learning framework.
PloS one
Nov 21, 2024
The integration of IoT (Internet of Things) devices has emerged as a technical cornerstone in the landscape of modern agriculture, revolutionising the way farming practises are viewed and managed. Smart farming, enabled by interconnected sensors and ...
Fuzzy multi-objective optimization for sustainable agricultural water management of irrigation networks.
Journal of environmental management
Nov 20, 2024
Sustainable water resource management in arid and water deficit regions requires optimal use of water resources due to competition among different water sectors. The purpose of this study is to model uncertainties in economic and hydro-climatic varia...
Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach.
Environment international
Nov 19, 2024
Pesticides typically co-occur in agricultural surface waters and pose a potential threat to human and ecosystem health. As pesticide screening in global agricultural surface waters is an immense analytical challenge, a detailed risk picture of pestic...
Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed.
Journal of environmental management
Nov 19, 2024
Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water qual...
Sunflower mapping using machine learning algorithm in Google Earth Engine platform.
Environmental monitoring and assessment
Nov 18, 2024
The sunflower crop is one of the most pro sources of vegetable oil globally. It is cultivated all around the world including Haryana, in India. However, its mapping is limited due to the requirement of huge computation power, large data storage capac...
Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence.
Journal of hazardous materials
Nov 15, 2024
Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health i...
Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.
Scientific reports
Nov 15, 2024
This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervas...
Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.
Environmental monitoring and assessment
Nov 8, 2024
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agr...
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