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Oryza

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Impact of economic indicators on rice production: A machine learning approach in Sri Lanka.

PloS one
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...

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration.

Journal of the science of food and agriculture
BACKGROUND: Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of mo...

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

Sensors (Basel, Switzerland)
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...

PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics.

Genes
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...

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
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...

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications
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...

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
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...

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Environmental monitoring and assessment
Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause eco...

Deep recognition of rice disease images: how many training samples do we really need?

Journal of the science of food and agriculture
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...

Discrimination of internal crack for rice seeds using near infrared spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
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 ...