AIMC Topic: Oryza

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Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV.

Pest management science Sep 12, 2024
BACKGROUND: Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identi...
Plant Leaves Remote Sensing Technology Oryza Animals Deep Learning Unmanned Aerial Devices Plant Diseases Moths
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Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

Journal of the science of food and agriculture Sep 2, 2024
BACKGROUND: Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotyp...
Soil Water Hyperspectral Imaging Least-Squares Analysis Chlorophyll Plant Leaves Oryza Neural Networks, Computer
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Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Journal of food science Sep 1, 2024
Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree...
Machine Learning China Edible Grain Image Processing, Computer-Assisted Oryza Color Food Handling
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Yield prediction for crops by gradient-based algorithms.

PloS one Aug 26, 2024
A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the var...
Oryza Machine Learning Temperature Crops, Agricultural Algorithms Agriculture
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Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment Aug 24, 2024
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...
Machine Learning Soil Oryza Cadmium Crops, Agricultural Soil Pollutants Triticum China Environmental Monitoring
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Predicting rice phenology across China by integrating crop phenology model and machine learning.

The Science of the total environment Aug 21, 2024
This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and...
Climate Seasons Crops, Agricultural Agriculture Oryza Machine Learning China
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Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR.

Food chemistry Aug 2, 2024
Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (M...
Oryza High-Throughput Screening Assays Seeds Phenols Machine Learning Antioxidants Spectroscopy, Fourier Transform Infrared
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ESforRPD2: Expert System for Rice Plant Disease Diagnosis.

F1000Research Jul 11, 2024
One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this pro...
Expert Systems Indonesia Oryza Plant Diseases
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A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment Jul 10, 2024
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental f...
Oryza Metals, Heavy Environmental Monitoring Soil Pollutants Neural Networks, Computer Machine Learning
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Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.

Scientific reports Jun 26, 2024
Ensuring the security of China's rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil...
Neural Networks, Computer Machine Learning Agriculture Oryza Soil Climate China
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