AIMC Topic: Oryza

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Surface-enhanced Raman spectroscopy charged probes under inverted superhydrophobic platform for detection of agricultural chemicals residues in rice combined with lightweight deep learning network.

Analytica chimica acta Apr 24, 2023
In this study, surface-enhanced Raman spectroscopy (SERS) charged probes and an inverted superhydrophobic platform were used to develop a detection method for agricultural chemicals residues (ACRs) in rice combined with lightweight deep learning netw...
Spectrum Analysis, Raman Metal Nanoparticles Deep Learning Agrochemicals Chlormequat Hydrophobic and Hydrophilic Interactions Oryza
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Deep-learning-based automatic evaluation of rice seed germination rate.

Journal of the science of food and agriculture Nov 24, 2022
BACKGROUND: Rice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving ima...
Humans Germination Deep Learning Seeds Oryza Plant Breeding
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Simulation of Transmission System of Crawler Self-propelled Rotary Tiller Based on Deep Learning.

Computational intelligence and neuroscience Jul 30, 2022
Because of its good performance, crawler-type running gear plays a very important role in the fields of modern agriculture. This article aims to study the construction of the drive system of the crawler self-propelled rotary tiller with the deep lear...
Deep Learning Soil Oryza Agriculture
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An insight into the act of iron to impede arsenic toxicity in paddy agro-system.

Journal of environmental management May 20, 2022
Surplus research on the widespread arsenic (As) revealed its disturbing role in obstructing the metabolic function of plants. Also, the predilection of As towards rice has been an interesting topic. Contrary to As, iron (Fe) is an essential micronutr...
Oryza Soil Pollutants Soil Prospective Studies Arsenic Plant Roots Iron
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Research on Rice Yield Prediction Model Based on Deep Learning.

Computational intelligence and neuroscience Apr 26, 2022
Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people'...
Image Processing, Computer-Assisted Oryza Neural Networks, Computer Deep Learning Humans
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GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

Methods (San Diego, Calif.) Feb 9, 2022
MOTIVATION: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. ...
DNA Oryza DNA Methylation Deep Learning Adenine
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Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices.

Sensors (Basel, Switzerland) Jan 18, 2022
Accurate crop yield forecasting is essential in the food industry's decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, howe...
Reproducibility of Results Oryza Algorithms Machine Learning
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HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds.

Sensors (Basel, Switzerland) Dec 8, 2021
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named to provide hyperspectr...
Neural Networks, Computer Oryza Support Vector Machine Spectrum Analysis
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Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea.

The Science of the total environment Aug 19, 2021
Prediction of rice yields at pixel scale rather than county scale can benefit crop management and scientific understanding because it is useful for monitoring how crop yields respond to various agricultural systems and environmental factors. In this ...
Neural Networks, Computer Deep Learning Democratic People's Republic of Korea Oryza Agriculture
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A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants.

The New phytologist Aug 12, 2021
Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep-learning approache...
Oryza Genome Deep Learning Epigenomics Epigenesis, Genetic DNA Methylation
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