Applicability of computer vision in seed identification: deep learning, random forest, and support vector machine classification algorithms

Bao, Francielli; Bambil, Deborah

doi:10.1590/0102-33062020abb0361

Cited by 18 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Four different classification models were built using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) [ 38 , 39 , 40 ], and CatBoost; the models were parameterized using empirical settings, as shown in Table 7 . To ensure the reliability of the experimental results and to avoid the chance of single experimental results, several experiments were conducted on different models.…”

Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.

show abstract

Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Machine learning-based image processing techniques have been successfully applied to detect seed quality with the advancement of computer vision technology [6][7][8]. The researchers conduct seed quality assessment by extracting features such as texture, color and shape of the seed images.…”

Section: Introductionmentioning

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

“…Computer vision has been extensively applied in agriculture, which are widely used in plant disease identification, 5 , 6 plant species identification, 7 , 8 and crop yield estimation 9 , 10 . At first, seeds are classified by machine learning methods that extract texture, shape, color, and other characteristics of the seeds manually 11 . In the 1990s, computer technology was used to successfully distinguish damaged corn 12 and wheat 13 based on parameters such as seed morphology.…”

Section: Introductionmentioning

confidence: 99%

“…9,10 At first, seeds are classified by machine learning methods that extract texture, shape, color, and other characteristics of the seeds manually. 11 In the 1990s, computer technology was used to successfully distinguish damaged corn 12 and wheat 13 based on parameters such as seed morphology. After that, scholars continued to identify seeds by texture, shape, color, and other features.…”

Section: Introductionmentioning

confidence: 99%

Research on plant seeds recognition based on fine-grained image classification

Yuan,

Dong,

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

Seed phenomics is a comprehensive assessment of complex seed traits, and seed classification is an indispensable step. Plant seed recognition is of great significance in agricultural production, ecological environment, and biodiversity. However, some traditional artificial plant seed classification methods are expensive, time consuming, and laborious. Therefore, there is a need that cannot be ignored for a method to improve the situation. Artificial intelligence is making a huge impact on various fields through its perception, reasoning, and learning capabilities. A challenge in pratacultural research, the rapid auto-identification of plant seeds, might be better resolved by the integration of computer vision. For the lack of a public seed dataset for the training of models, we established a dataset called LZUPSD, which includes images of 88 different species of seeds. We explored methods to achieve fine-grained seed classification using convolutional neural networks and tried to apply a transformer to it. The method has the highest accuracy of more than 95%. The method is able to identify plant seeds automatically with high speed, low cost, and high accuracy. It results in a more efficient plant seed recognition method. At the same time, we have established a platform where users can upload pictures to obtain seed information. In addition, our dataset will be released to the public in the next phase in order to share with interested researchers.

show abstract

Applicability of computer vision in seed identification: deep learning, random forest, and support vector machine classification algorithms

Cited by 18 publications

References 35 publications

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

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

Research on plant seeds recognition based on fine-grained image classification

Contact Info

Product

Resources

About