AIMC Topic: Triticum

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Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.

Scientific reports Sep 30, 2024
This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: l...
Algorithms Genotype Support Vector Machine Triticum
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Evaluation of crop water stress index of wheat by using machine learning models.

Environmental monitoring and assessment Sep 23, 2024
The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops...
Crops, Agricultural Agricultural Irrigation India Agriculture Triticum Dehydration Environmental Monitoring Machine Learning Temperature Water Neural Networks, Computer
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Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework.

The Science of the total environment Sep 14, 2024
Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage...
Machine Learning China Agriculture Ozone Triticum Climate Change Environmental Monitoring Air Pollutants Air Pollution
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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...
Soil Pollutants Triticum Cadmium Crops, Agricultural Oryza Soil China Environmental Monitoring Machine Learning
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Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Toxins Aug 13, 2024
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the major concerns associated with spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and anim...
Plant Diseases Triticum Edible Grain Mycotoxins Fusarium Deep Learning Fungicides, Industrial Food Contamination
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Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

The Science of the total environment Aug 11, 2024
Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging....
Soil Pollutants Triticum Edible Grain Machine Learning Environmental Monitoring Metals, Heavy Metallurgy
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Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.

Food chemistry Jul 29, 2024
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quan...
Seeds Spectroscopy, Near-Infrared Triticum Deep Learning High-Throughput Screening Assays Hyperspectral Imaging Edible Grain Nutrients Nutritive Value
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Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment Jul 10, 2024
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...
Soil Trace Elements Environmental Monitoring Soil Pollutants Zinc Neural Networks, Computer Rhizosphere Triticum Edible Grain
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Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

The plant genome Jun 9, 2024
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivaleno...
Disease Resistance Phenotype Fusarium Plant Breeding Genomics Phenomics Plant Diseases Deep Learning Triticum
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Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

International journal of biometeorology May 28, 2024
Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab, wheat face challenge by different diseases so the study was conducted for two locations viz. Ludhi...
Plant Diseases Models, Theoretical Machine Learning India Triticum Weather
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