AIMC Topic: Triticum

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The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models.

Scientific reports Nov 14, 2023
Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increas...
Neural Networks, Computer Machine Learning Agriculture Logistic Models Triticum
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Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jun 19, 2023
Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 25...
Algorithms Spectroscopy, Near-Infrared Support Vector Machine Least-Squares Analysis Triticum Flour
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Evaluation of AquaCrop and intelligent models in predicting yield and biomass values of wheat.

International journal of biometeorology Feb 28, 2023
AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management facto...
Neural Networks, Computer Algorithms Biomass Triticum Climate
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Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data.

Computational intelligence and neuroscience Nov 24, 2022
The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant prot...
Neural Networks, Computer Support Vector Machine Diagnostic Imaging Triticum
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Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.

Environmental science and pollution research international Oct 20, 2022
Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an...
Neural Networks, Computer Algorithms Triticum Spatial Analysis Uncertainty
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Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3.

Sensors (Basel, Switzerland) Oct 8, 2022
Wheat, one of the most important food crops in the world, is usually harvested mechanically by combine harvesters. The impurity rate is one of the most important indicators of the quality of wheat obtained by mechanized harvesting. To realize the onl...
Neural Networks, Computer Triticum Online Systems
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Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Scientific reports Feb 25, 2022
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an...
Neural Networks, Computer Machine Learning Soil Triticum Seasons
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Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model.

Sensors (Basel, Switzerland) Oct 14, 2021
The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has ...
Neural Networks, Computer Agriculture Triticum
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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.

Sensors (Basel, Switzerland) Sep 30, 2021
Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identi...
Deep Learning Support Vector Machine Triticum Plant Diseases Basidiomycota
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Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

The plant genome Sep 5, 2021
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Beca...
Artificial Intelligence Deep Learning Genomics Bayes Theorem Triticum Genome, Plant Plant Breeding
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