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As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world's largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. [ABSTRACT FROM AUTHOR]

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method. [ABSTRACT FROM AUTHOR]

With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation. [ABSTRACT FROM AUTHOR]