Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a ...practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R
) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R
= 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.
The pandemic in 2020 made online learning the widely used modality of teaching in several countries and it has also entered the spotlight of educational research. However, online learning has always ...been a challenge for disciplines (engineering, biology, and art) that require hands-on practice. For art teaching or training, online learning has many advantages and disadvantages. How art teachers embrace and adapt their teaching for online delivery remains an unanswered question. This research examines 892 art teachers' attitudes toward online learning, using learning environment, need satisfaction, mental engagement, and behavior as predictors. Structural equation modeling was used to explore the relationship between these four dimensions during these teachers' participation in an online learning program. The results reveal significant correlations between the learning environment, need satisfaction, mental engagement, and behavior. Moreover, this study reveals the group characteristics of art teachers, which can actually be supported by online learning programs. These findings provide insights into how art teachers view and use online learning, and thus can shed lights on their professional development.
Using emotion detection technologies from biophysical signals, this study explored how emotion evolves during learning process and how emotion feedback could be used to improve learning experiences. ...This article also described a cutting-edge pervasive e-Learning platform used in a Shanghai online college and proposed an affective e-Learning model, which combined learners' emotions with the Shanghai e-Learning platform. The study was guided by Russell's circumplex model of affect and Kort's learning spiral model. The results about emotion recognition from physiological signals achieved a best-case accuracy (86.3%) for four types of learning emotions. And results from emotion revolution study showed that engagement and confusion were the most important and frequently occurred emotions in learning, which is consistent with the findings from AutoTutor project. No evidence from this study validated Kort's learning spiral model. An experimental prototype of the affective e-Learning model was built to help improve students' learning experience by customizing learning material delivery based on students' emotional state. Experiments indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%.
Maturity level-based classification system plays an essential role in the design of tomato harvesting robot. Traditional knowledge-based systems are unable to meet the current production management ...requirements of precision picking, because they are time-consuming and have low accuracy. Our research proposes an improved deep learning-based classification method that improves the accuracy and scalability of tomato ripeness with a small amount of training data. This study was on the relationship between different dataset augmentation methods and prediction results of final classification task. We implemented classification systems based on convolutional neural network (CNN), by training and validating the model on different augmented datasets and tried to choose an optimal augmentation method for datasets. The experimental results showed an average accuracy of 91.9% with a less than 0.01-s prediction time. Compared to the existing methods, our solution achieved better prediction results both in terms of accuracy and time consumption. Moreover, this is a versatile method and can be extended to other related fields.
Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is ...error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step.
We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset.
The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.
Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease ...classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification.
Computer vision-based fire detection is one of the crucial tasks in modern surveillance system. In recent years, the convolutional neural network (CNN) has become an active topic because of its high ...accuracy recognition rate in a wide range of applications. How to reliably and effectively solve the problems of flame detection, however, has still been a challenging problem in practice. In this paper, we proposed a novel flame detection algorithm based on CNN in real time by processing the video data generated by an ordinary camera monitoring a scene. Firstly, to improve the efficiency of recognition, a candidate target area extraction algorithm is proposed for dealing with the suspected flame area. Secondly, the extracted feature maps of candidate areas are classified by the designed deep neural network model based on CNN. Finally, the corresponding alarm signal is obtained by the classification results. The experimental results show that the proposed method can effectively identify fire and achieve higher alarm rate in the homemade database. The proposed method can effectively realize the real-time performance of fire warning in practice.
Effective and efficient fruit detection is considered crucial for designing automated robot (AuRo) for yield estimation, disease control, harvesting, sorting, and grading. Several fruit detection ...schemes for designing AuRo have been developed during the last decades. However, conventional fruit detection methods are deficient in the real-time response, accuracy, and extensibility. This paper proposes an improved multi-task cascaded convolutional network-based intelligent fruit detection method. This method has the capability to make the AuRo work in real time with high accuracy. Moreover, based on the relationship between the diversity samples of the dataset and the parameters of neural networks' evolution, this paper presents an improved augmented method, a procedure that is based on image fusion to improve the detector performance. The experiment results demonstrated that the proposed detector performed immaculately both in terms of accuracy and time-cost. Furthermore, the extensive experiment also demonstrated that the proposed technique has the capacity and good portability to work with other akin objects conveniently.
Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement ...scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.
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