A methodology for sorting haploid and diploid corn seed using terahertz time domain spectroscopy and machine learning

Taylor, Jared L.; Chou, Chien-Ping T.; Bond, Leonard J.

doi:10.1063/1.5099809

Cited by 11 publications

(8 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This analysis has been done on waveform data and aligned the terahertz time-domain spectroscopy (THz-TDS) with the machine learning algorithm PNN. The result of their classification rate showed 75% accuracy with 5-fold cross-validation [10].…”

Section: Related Workmentioning

confidence: 99%

“…In recent studies, machine learning techniques have been observed more frequently to perform seed classification of various crops, fruits and vegetables. Most of these studies have been conducted on a single genre of seed (e.g., weed seeds [3], cottonseeds [4], rice seeds [5,6], oat seeds [7], sunflower seeds [8], tomato seeds [9] and corn [10,11]) with varying purposes. These included observing germination and vigour detection, purification and growth stages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Convolution Neural Network-Based Seed Classification System

Hamid²,

et al. 2020

View full text Add to dashboard Cite

Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model checkpointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Convolution Neural Network-Based Seed Classification System

Hamid²,

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Most previous research has focused on the combination of a single ML model in MSI analysis [ 40 , 41 ]. In this study, a new SEL model, containing three-level models, was superior to all basic models according to the metrics of overall accuracy, kappa, sensitivity, specificity, and precision.…”

Section: Discussionmentioning

confidence: 99%

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Jia

Sun

et al. 2022

Sensors

View full text Add to dashboard Cite

Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.

show abstract

“…For this reason, an expert system is needed for the fast, sensitive, and automatic selection of haploid seeds (Altuntaş et al, 2019). Taylor et al (2019) collected the time-domain waveforms of mixed-state haploid and diploid seeds using terahertz timedomain spectroscopy. The watershed image segmentation technique was used to improve and reduce accordingly in the data set.…”

Section: Introductionmentioning

confidence: 99%

“…A probabilistic neural network was used as a classifier. In addition, an average classification success of 75% was obtained with the five-fold cross-validation technique (Taylor et al, 2019). Altuntas et al (2019), developed a system to identify haploid maize seeds using a convolutional neural network (CNN) and transfer learning approaches.…”

Section: Introductionmentioning

confidence: 99%

DeepMaizeNet: A novel hybrid approach based on CBAM for implementing the doubled haploid technique

Ayaz,

Kutlu,

Cömert

2023

Agronomy Journal

View full text Add to dashboard Cite

Maize (Zea mays L.) is an important cereal plant in the family of wheatgrass cultivated all over the world. With the increase in human population and environmental factors, the need for maize plants is increasing day by day. One of the efficient methods of increasing production of the maize is maize breeding. The most effective and rapid method for maize breeding is the doubled haploid (DH) technique. This technique reduces maize breeding time and increases productivity. There are different selection methods to select haploid maize seeds in the maize breeding process. Among these selection methods, the most common and most successful selection method is the visual checking of the R1‐Navajo marker. Maize seed separation by hand is a time‐consuming and error‐prone operation. It is labor‐intensive and very tiring; therefore, it is essential to develop a fast and highly accurate intelligent system that separates diploid and haploid maize seeds from each other. This study presents a pioneering approach, introducing the DeepMaizeNet, a hybrid deep learning model that showcases its prowess in accurately classifying haploid maize seeds. The classification of haploid seeds holds significant value for the DH technique, and the proposed model's success is a promising step toward enhanced efficiency. The proposed hybrid model exploits some new techniques such as convolution block attention module, hypercolumn, 2D upsampling, and residual block. For the assessment of the proposed model, the five‐fold cross‐validation technique is employed. The result shows that DeepMaizeNet provides a promising performance by achieving 94.13% accuracy, 94.91% F1‐score, and 97.27% sensitivity.

show abstract

A methodology for sorting haploid and diploid corn seed using terahertz time domain spectroscopy and machine learning

Cited by 11 publications

References 52 publications

A Convolution Neural Network-Based Seed Classification System

A Convolution Neural Network-Based Seed Classification System

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

DeepMaizeNet: A novel hybrid approach based on CBAM for implementing the doubled haploid technique

Contact Info

Product

Resources

About