AIMC Topic: Agriculture

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Research on soil moisture prediction model based on deep learning.

PloS one
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...

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture.

Sensors (Basel, Switzerland)
Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offli...

Field evaluation of an unmanned aerial vehicle (UAV) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat.

Pest management science
BACKGROUND: Unmanned aerial vehicles (UAVs) are a recently developed aerial spraying technology. However, the effect of spray volume variation on deposition and pesticide control efficacy is unknown. The effect of three UAV spray volumes (9.0, 16.8 a...

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.

PloS one
Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and...

Do reductions in agricultural field drainage during the growing season impact bacterial densities and loads in small tile-fed watersheds?

Water research
Predicting bacterial levels in watersheds in response to agricultural beneficial management practices (BMPs) requires understanding the germane processes at both the watershed and field scale. Controlling subsurface tile drainage (CTD) is a highly ef...

Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.

PloS one
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge...

Forecasting the spatiotemporal variability of soil CO emissions in sugarcane areas in southeastern Brazil using artificial neural networks.

Environmental monitoring and assessment
Carbon dioxide (CO) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, sinc...

Deep learning for supervised classification of spatial epidemics.

Spatial and spatio-temporal epidemiology
In an emerging epidemic, public health officials must move quickly to contain the spread. Information obtained from statistical disease transmission models often informs the development of containment strategies. Inference procedures such as Bayesian...

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India.

International journal of biometeorology
Rice is generally grown under completely flooded condition and providing food for more than half of the world's population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning populat...

Leaching and sorption of neonicotinoid insecticides and fungicides from seed coatings.

Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes
Seed coatings are a treatment used on a variety of crops to improve production and offer protection against pests and fungal outbreaks. The leaching of the active ingredients associated with the seed coatings and the sorption to soil was evaluated un...