AIMC Topic: Crops, Agricultural

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Sunflower mapping using machine learning algorithm in Google Earth Engine platform.

Environmental monitoring and assessment
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...

Deep learning-based rice pest detection research.

PloS one
With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-tim...

Improving crop production using an agro-deep learning framework in precision agriculture.

BMC bioinformatics
BACKGROUND: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors i...

Big data and artificial intelligence-aided crop breeding: Progress and prospects.

Journal of integrative plant biology
The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of ...

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

PeerJ
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...

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Journal of environmental quality
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...

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

The Science of the total environment
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...

Parametric seasonal-trend autoregressive neural network for long-term crop price forecasting.

PloS one
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...

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
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 ...

Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

Sensors (Basel, Switzerland)
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...