AIMC Topic: Agricultural Irrigation
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Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.
Environmental science and pollution research international
Jan 9, 2023
Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors ...
Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.
PloS one
May 31, 2019
Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-base...
Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production.
Sensors (Basel, Switzerland)
Apr 16, 2019
The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI...
Research on soil moisture prediction model based on deep learning.
PloS one
Apr 3, 2019
Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteri...
Review of water quality criteria for water reuse and risk-based implications for irrigated produce under the FDA Food Safety Modernization Act, produce safety rule.
Environmental research
Dec 21, 2018
Questions related to the safety of alternative water sources, such as recycled water or reclaimed water (including grey water, produced water, return flows, and recycled wastewater), for produce production have been largely un-explored at the detail ...
Vineyard water status assessment using on-the-go thermal imaging and machine learning.
PloS one
Feb 1, 2018
The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estima...
Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea.
Environmental science and pollution research international
Mar 18, 2015
This research aimed at developing comprehensive assessments of physicochemical quality of groundwater for drinking and irrigation purposes at Dalcheon in Ulsan City, Korea. The mean concentration of major ions represented as follows: Ca (94.3 mg/L) >...
Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.
Talanta
Aug 13, 2014
Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application beca...
Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence.
Scientific reports
May 14, 2025
This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress. Two field experiments were conducted with three irrigation regimes: 100% (B100)...
Integrated irrigation of water and fertilizer with superior self-correcting fuzzy PID control system.
PloS one
Jan 1, 2025
To address the fixed-parameter limitations of traditional PID control (e.g., excessive overshoot, prolonged settling time, poor adaptability to nonlinearities) and the insufficient real-time adjustment capability of conventional fuzzy PID control, wh...
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