Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Wu, Nayiyuan; Liu, Fei; Meng, Fanrong; Zhang, Chu; He, Yong

doi:10.3389/fbioe.2021.696292

Cited by 21 publications

(19 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When T was conducted, f ð:Þ constructed a model using fx i , p i g in the domain. The goal of transfer learning is to improve the performance of the predictive function in the target domain D T with using the knowledge learned from the source domain D S [37]. Fine-tuning is a common method in deep transfer learning.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

Self Cite

View full text Add to dashboard Cite

Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

show abstract

Section: Data Analysis Methodsmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Transfer learning is applying knowledge learned in one source domain to another related target domain. Annotating large amounts of data in convolutional neural networks can be prevented, the model’s dependence on data can be reduced, and the training efficiency of the model can be enhanced [ 35 – 37 ]. This study was motivated by this and trained the Impro-ResNet50 model using transfer learning.…”

Section: Methodsmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

show abstract

“…In cotton, deep learning has already been applied to detecting seedlings (Jiang et al, 2019), flowers (Jiang et al, 2020), bolls (Xu et al, 2021), leaf lesions (Caldeira et al, 2021;Liang, 2021), segmenting roots in soil (Shen C. et al, 2020), and identifying seeds from different cultivars (Zhu et al, 2019;Wu et al, 2021). Whilst these studies are encouraging and highlight some of the cotton traits and characteristics which can benefit from deep learning approaches, the accuracy and scale of the methodologies developed so far are not compatible with, and practical enough for, a commercial breeding effort.…”

Section: Phenomicsmentioning

confidence: 99%

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century

et al. 2022

View full text Add to dashboard Cite

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program’s commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

show abstract

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Cited by 21 publications

References 50 publications

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century

Contact Info

Product

Resources

About