AIMC Topic: Arachis

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Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds.

Scientific reports
Effective weed detection for precise management remains a pertinent issue in modern agriculture. In this study, hyperspectral imaging (HSI) was combined with machine learning (ML) to differentiate between peanut plants and four common weeds found in ...

Integrative genomics and genetics from evolutionary insights to precision breeding in peanuts (Arachis Hypogaea L.).

Functional & integrative genomics
Peanut (Arachis hypogaea L.), a globally important oilseed crop, increasingly challenged by rising edible oil demands as well as biotic and abiotic stresses. This review synthesizes recent advances in peanut genomics, evolutionary biology, and breedi...

Assessing the transfer of Cd and As from co-contaminated soil to peanut (Arachis hypogaea L.): prediction models and soil thresholds.

Environmental pollution (Barking, Essex : 1987)
In China, the co-contamination of soil with cadmium (Cd) and arsenic (As) is one of the most severe forms of combined pollution. Modeling the transfer of Cd and As from co-contaminated soil to crops has not been thoroughly studied. In this study, fiv...

Artificial intelligence-enabled microsphere imaging immunosensor based on magnetic metal-organic frameworks-assisted sample pretreatment for detecting aflatoxin B in peanuts.

Journal of hazardous materials
Sensitive and rapid detection of aflatoxin B (AFB) is vital for safeguarding food safety, considering its potent carcinogenic toxicity. Herein, an artificial intelligence-enabled microsphere imaging (AI-MI) immunosensor based on magnetic metal-organi...

Intermediate data fusion improves the accuracy of near-infrared spectroscopy and Raman spectroscopy for the detection of aflatoxin B1 in peanuts.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study developed a convolutional neural network (CNN) model based on feature-level data fusion for quantitatively detecting aflatoxin B1 (AFB1) in peanuts. Using a portable near-infrared (NIR) spectrometer and a Raman spectrometer, NIR and Raman ...

Aflatoxin detection in naturally contaminated peanuts based on vision transformer and multi-scale convolutional fusion.

Food chemistry
Aflatoxin is a highly toxic substance found in peanuts, posing a serious threat to human health. To address this issue, an improved 1D-MCFViT model combining the Vision Transformer with multi-scale convolutional fusion is proposed to detect aflatoxin...

Enhancing the application of near-infrared spectroscopy in grain mycotoxin detection: An exploration of a transfer learning approach across contaminants and grains.

Food chemistry
Cereals are a primary source of sustenance for humanity. Monitoring, controlling, and preventing mycotoxins in cereals are vital for ensuring the safety of the cereals and their derived products. This study introduces transfer learning strategies int...

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...

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.

Network (Bristol, England)
Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images ar...

Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System.

Network (Bristol, England)
Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep ...