Recently, deep learning-based fruit detection applications have been widely used in the modern fruit industry; however, the training data labeling process remains a time-consuming and labor-intensive process. Auto labeling can provide a convenient and efficient data source for constructing smart orchards based on deep-learning technology. In our previous study, based on a labeled source domain fruit dataset, we used a generative adversarial network and a fruit detection model to achieve auto labeling of unlabeled target domain fruit images. However, since the current method uses one species source domain fruit to label multiple species target domain fruits, there is a problem of the domain gap in both the foreground and the background between the training data (retaining the source domain fruit label information) and the application data (target domain fruit images) of the fruit detection model. Therefore, we propose a domain-adaptive anchor-free fruit detection model, DomAda-FruitDet, and apply it to the previously proposed fruit labeling method to further improve the accuracy. It consists of 2 design aspects: (a) With a foreground domain-adaptive structure based on double prediction layers, an anchor-free method with multiscale detection capability is constructed to generate adaptive bounding boxes that overcome the foreground domain gap; (b) with a background domain-adaptive strategy based on sample allocation, we enhance the ability of the model to extract foreground object features to overcome the background domain gap. As a result, the proposed method can label actual apple, tomato, pitaya, and mango datasets, with an average precision of 90.9%, 90.8%, 88.3%, and 94.0%, respectively. In conclusion, the proposed DomAda-FruitDet effectively addressed the problem of the domain gap and improved effective auto labeling for fruit detection tasks.

Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor.

In buildings, the heating ventilation and air conditioning system (HVAC) creates a comfortable environment for indoor occupants by setting a temperature strategy. However, this approach leads to unreasonable indoor environmental comfort and wasted energy because it does not dynamically adjust to changes in environmental and has a long response time. In this study, a high-precision human comfort prediction method for indoor personnel based on time-series analysis is proposed as the control strategy for HVAC systems. The method includes the data pre-processing module, the class imbalance processing module, and the human comfort network model module. We propose the Human-Comfort Bi-directional Long Short-Term Memory (HC-BiLSTM) network to achieve a better human comfort prediction, and the Synthetic Minority Oversampling Technique for Time-series (SMOTE-TS) algorithm to solve the class imbalance problem in human comfort dataset. A public dataset collected in Pennsylvania, USA, was selected for this study to validate the performance of the proposed method. The experimental results show that the human comfort prediction method proposed in this study achieves 0.9482 and 0.9659 on Macro-averaging and Micro-averaging, respectively, which is the highest accuracy in the known related research.