AIMC Topic: Agriculture

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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)...

A hybrid machine learning model for predicting agricultural production costs: Integrating economic sensitivity analysis and environmental factors in Egypt.

Journal of environmental management
Accurate prediction of agricultural production costs is crucial for sustainable development in Egypt, where productivity is highly sensitive to fluctuating economic and environmental conditions. This study introduces a hybrid machine learning model t...

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...

Making waves: Is water quality trading a false promise for balancing ecology and economy?

Water research
Agriculture is a major contributor to water pollution through nutrient runoff and excessive water use, exacerbating global water scarcity and ecosystem degradation. Water Quality Trading (WQT) has emerged as a market-based mechanism to address this i...