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

Scientific reports
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...

Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify thr...

Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.

Scientific reports
This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervas...

An effective method for detecting the wheat freshness by integrating biophotonics and machine learning algorithm.

Scientific reports
The accurate and timely assessment of wheat freshness is not only a complex scientific endeavor but also a critical aspect of grain storage safety. This study introduces an innovative approach for evaluating wheat freshness by integrating machine lea...

Wheat disease recognition method based on the SC-ConvNeXt network model.

Scientific reports
When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model's recognition perfor...

Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This st...

Cropformer: An interpretable deep learning framework for crop genomic prediction.

Plant communications
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models ...

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

International journal of biometeorology
In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34 years were collected from t...

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

Biosensors & bioelectronics
The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition parti...

MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...