AIMC Topic: Agriculture

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WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification.

Scientific reports
Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach...

Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems.

Scientific reports
As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop produ...

AI and IoT-powered edge device optimized for crop pest and disease detection.

Scientific reports
Climate change exacerbates the challenges of maintaining crop health by influencing invasive pest and disease infestations, especially for cereal crops, leading to enormous yield losses. Consequently, innovative solutions are needed to monitor crop h...

Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian orchards.

Scientific reports
Durian is a valuable tropical fruit whose pollination heavily relies on bats and nocturnal insects. However, environmental degradation and pesticide usage have reduced insect populations, leading to inefficient natural pollination. This study propose...

Edaphic and meteorological parameters as determinants of radon exhalation and its environmental implication in Peruvian agroecosystems.

Scientific reports
Radon exhalation is a natural process by which atoms of the radioactive gas radon diffuse in the soil and then exhale to an indoor and/or outdoor environment. High radon concentration levels, possibly from high radon exhalation rate levels, can gener...

Plant leaf disease detection using vision transformers for precision agriculture.

Scientific reports
Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs)...

In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics.

Science robotics
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, w...

Oil Palm Fruits Dataset in Plantations for Harvest Estimation Using Digital Census and Smartphone.

Scientific data
This article presents a dataset of oil palm Fresh Fruit Bunches (FFBs) images from commercial plantations in Central Kalimantan, Indonesia, focusing on five maturity stages: Unripe, Underripe, Ripe, Flower, and Abnormal. The data collection involved ...

Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting.

PloS one
The fluctuations in corn prices not only increase uncertainty in the market but also affect farmers' planting decisions and income stability, while also impeding crucial investments in sustainable agricultural practices. Collectively, these factors j...

A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture.

Scientific reports
Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many count...