AIMC Topic: Agriculture

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Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system.

PloS one
As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies ...

Deep learning based agricultural pest monitoring and classification.

Scientific reports
Precise pest classification plays an essential role in smart agriculture. Crop yields are severely impacted by pest damage, which poses a critical challenge for agricultural production and the economy. Identifying pests is of utmost importance, but m...

Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods.

Scientific reports
Climate change and environmental degradation pose a significant threat to the global community. Soil management is one of the critical factors for achieving climate neutrality, as plants and soils together currently absorb approximately 30% of the CO...

Leveraging ML to predict climate change impact on rice crop disease in Eastern India.

Environmental monitoring and assessment
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for...

Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing.

Food research international (Ottawa, Ont.)
The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, im...

Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.

Scientific reports
Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low...

Driving factors of TOC concentrations in four different types of estuaries (canal, urban, agricultural, and natural estuaries) identified by machine learning technique.

Marine pollution bulletin
Mangroves are among the most significant organic carbon sinks on Earth. However, the drivers of mangrove carbon remain poorly understood due to the lack of data on organic carbon across different types of estuaries. In this study, boosted regression ...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Environmental pollution (Barking, Essex : 1987)
Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published litera...

Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning.

Chemosphere
Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impracti...

BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8.

Scientific reports
Accurate classification of cherry varieties is crucial for their economic value and market differentiation, yet their genetic diversity and visual similarity make manual identification challenging, hindering efficient agricultural and trade practices...