Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

TA Shaikh, T Rasool, FR Lone - Computers and Electronics in Agriculture, 2022 - Elsevier
The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …

[HTML][HTML] An overview of smart irrigation systems using IoT

K Obaideen, BAA Yousef, MN AlMallahi, YC Tan… - Energy Nexus, 2022 - Elsevier
Countries are working into making agriculture more sustainable by integrating different
technologies to enhance its operation. Implementing improvements in irrigation systems is …

[HTML][HTML] The role of wastewater treatment in achieving sustainable development goals (SDGs) and sustainability guideline

K Obaideen, N Shehata, ET Sayed, MA Abdelkareem… - Energy Nexus, 2022 - Elsevier
The world is currently striving to achieve the globally adopted sustainable development
goals (SDGs). Exploring the role of technology in achieving the SDGs is critical for the …

Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches

JP Bharadiya, NT Tzenios… - Journal of Engineering …, 2023 - classical.goforpromo.com
The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …

[HTML][HTML] Crop yield prediction using machine learning: A systematic literature review

T Van Klompenburg, A Kassahun, C Catal - Computers and electronics in …, 2020 - Elsevier
Abstract Machine learning is an important decision support tool for crop yield prediction,
including supporting decisions on what crops to grow and what to do during the growing …

Technological revolutions in smart farming: Current trends, challenges & future directions

V Sharma, AK Tripathi, H Mittal - Computers and Electronics in Agriculture, 2022 - Elsevier
With increasing population, the demand for agricultural productivity is rising to meet the goal
of “Zero Hunger”. Consequently, farmers have optimized the agricultural activities in a …

[HTML][HTML] A review of ensemble learning algorithms used in remote sensing applications

Y Zhang, J Liu, W Shen - Applied Sciences, 2022 - mdpi.com
Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

R Sharma, SS Kamble, A Gunasekaran… - Computers & Operations …, 2020 - Elsevier
Agriculture plays an important role in sustaining all human activities. Major challenges such
as overpopulation, competition for resources poses a threat to the food security of the planet …

Artificial intelligence to improve the food and agriculture sector

R Ben Ayed, M Hanana - Journal of Food Quality, 2021 - Wiley Online Library
The world population is expected to reach over 9 billion by 2050, which will require an
increase in agricultural and food production by 70% to fit the need, a serious challenge for …

Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges

M Torky, AE Hassanein - Computers and Electronics in Agriculture, 2020 - Elsevier
Blockchain quickly became an important technology in many applications of precision
agriculture discipline. The need to develop smart P2P systems capable of verifying …