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Agriculture

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German sugar beet farmers' intention to use autonomous field robots for seeding and weeding.

Journal of environmental management
Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes towa...

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics.

Journal of environmental management
Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different l...

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Environmental science and pollution research international
Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change...

Yield prediction for crops by gradient-based algorithms.

PloS one
A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the var...

From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change.

The Science of the total environment
Accurate crop yield predictions are crucial for farmers and policymakers. Despite the widespread use of ensemble machine learning (ML) models in computer science, their application in crop yield prediction remains relatively underexplored. This study...

Predicting rice phenology across China by integrating crop phenology model and machine learning.

The Science of the total environment
This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and...

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata.

Environmental monitoring and assessment
Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone...

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation.

Journal of hazardous materials
Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing's central urban area. Results indicated...

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape.

Journal of environmental management
The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategie...

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

Phytopathology
In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, a...