AIMC Topic: Oryza

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A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Journal of the science of food and agriculture Jun 21, 2024
BACKGROUND: Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial fo...
Deep Learning Image Processing, Computer-Assisted Seeds Neural Networks, Computer Algorithms Oryza
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Impact of economic indicators on rice production: A machine learning approach in Sri Lanka.

PloS one Jun 21, 2024
Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models u...
Oryza Support Vector Machine Crops, Agricultural Machine Learning Crop Production Sri Lanka Agriculture
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Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

Journal of hazardous materials Jun 17, 2024
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...
Oryza Photosynthesis Oxidative Stress Machine Learning Organic Chemicals Soil Pollutants
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Deep recognition of rice disease images: how many training samples do we really need?

Journal of the science of food and agriculture Jun 15, 2024
BACKGROUND: With the rapid development of deep learning, the recognition of rice disease images using deep neural networks has become a hot research topic. However, most previous studies only focus on the modification of deep learning models, while l...
Plant Diseases Plant Leaves Oryza Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted
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Discrimination of internal crack for rice seeds using near infrared spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jun 1, 2024
It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy ...
Spectroscopy, Near-Infrared Support Vector Machine Least-Squares Analysis Discriminant Analysis Oryza Seeds Principal Component Analysis
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Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy May 19, 2024
The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers...
Algorithms Thailand Principal Component Analysis Least-Squares Analysis Spectroscopy, Near-Infrared Neural Networks, Computer Machine Learning Geography Discriminant Analysis Oryza
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TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications May 15, 2024
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target...
Crops, Agricultural Phenotype Plant Breeding Genotype Triticum Oryza Zea mays Neural Networks, Computer Machine Learning
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Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

Sensors (Basel, Switzerland) May 9, 2024
The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly...
Oryza Starch Riboflavin NADP Seeds Spectrometry, Fluorescence Algorithms Support Vector Machine
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PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics.

Genes May 9, 2024
As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizin...
Genome, Plant Plant Breeding Genomics Polymorphism, Single Nucleotide Machine Learning Oryza
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Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.

Chemosphere May 6, 2024
The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery...
Fertilizers Composting Tanning Wetlands Metals, Heavy Animals Metals Oryza Sewage Soil Pollutants Machine Learning Soil
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