AIMC Topic: Crops, Agricultural

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Predicting the influence of extreme temperatures on grain production in the Middle-Lower Yangtze Plains using a spatially-aware deep learning model.

PeerJ Oct 18, 2024
Grain crops are vulnerable to anthropogenic climate change and extreme temperature events. Despite this, previous studies have often neglected the impact of the spatio-temporal distribution of extreme temperature events on regional grain outputs. Thi...
Crops, Agricultural Deep Learning China Edible Grain Climate Change
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Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Journal of environmental quality Oct 9, 2024
Soil nitrous oxide (NO) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at t...
Machine Learning Air Pollutants Soil Crops, Agricultural Nitrous Oxide Agriculture Environmental Monitoring
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Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

The Science of the total environment Oct 3, 2024
Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential...
Crops, Agricultural Soil China Machine Learning Soil Pollutants Agriculture Environmental Monitoring Cadmium Zinc
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Parametric seasonal-trend autoregressive neural network for long-term crop price forecasting.

PloS one Sep 26, 2024
Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autor...
Commerce Neural Networks, Computer Raphanus Seasons Forecasting Crops, Agricultural
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Co-exposure to microplastics and soil pollutants significantly exacerbates toxicity to crops: Insights from a global meta and machine-learning analysis.

The Science of the total environment Sep 24, 2024
Environmental contamination of microplastics (MPs) is ubiquitous worldwide, and co-contamination of arable soils with MPs and other pollutants is of increasing concern, and may lead to unexpected consequences on crop production. However, the overall ...
Microplastics Crops, Agricultural Environmental Monitoring Soil Pollutants Environmental Pollution Machine Learning Soil
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Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

Sensors (Basel, Switzerland) Sep 24, 2024
This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected on...
Plant Diseases Machine Learning Algorithms Solanum lycopersicum Soil India Support Vector Machine Internet of Things Crops, Agricultural
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Evaluation of crop water stress index of wheat by using machine learning models.

Environmental monitoring and assessment Sep 23, 2024
The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops...
Triticum Dehydration Agricultural Irrigation Crops, Agricultural Agriculture Environmental Monitoring Water India Machine Learning Temperature Neural Networks, Computer
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Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models.

Scientific reports Sep 17, 2024
In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food securi...
Oryza Seasons Crops, Agricultural Machine Learning Remote Sensing Technology Radar Neural Networks, Computer
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Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.

Sensors (Basel, Switzerland) Sep 14, 2024
The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages e...
Deep Learning Image Processing, Computer-Assisted Crops, Agricultural Cloud Computing Agriculture Neural Networks, Computer Algorithms Humans
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Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Phytopathology Sep 13, 2024
Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection...
Deep Learning Plant Leaves Crops, Agricultural Neural Networks, Computer Ascomycota Crop Production Plant Diseases Hordeum
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