Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.

Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjusted for a four-dimensional (4D) RGBD input to classify flowers, and their classification performances were compared with and without depth information. The experimental results show that the classification accuracy was improved with depth information, and the improved InceptionV3 network with RGBD achieved the highest classification accuracy (up to 98%), which means that the depth information can effectively reflect the characteristics of the flower bud and is helpful for the classification of the maturing status. These results have a certain significance for the intelligent classification and sorting of fresh flowers.

The Chinese furniture market has a high demand for wood floors. Manual defect detection in wood floors is inefficient and lacks stability in accuracy. It is necessary to conduct research on automatic defect detection in wood floors. To improve the accuracy of detecting small defects in wood floors, this paper proposed a new network based on YOLOv7. The new network is called the cascade center of gravity YOLOv7 (CCG-YOLOv7). This paper designed cascade efficient layer aggregation networks (C-ELAN), streamlined the CBS, replaced the ELAN with the C-ELAN, introduced the rapid supervised attention module to connect the backbone and head layers, and simplified the head layer of the YOLOv7 network. These methods improved the detection accuracy and speed for detecting small defects on wood floor surfaces. The improved network can effectively detect small defects on the wooden board surfaces, including knots, scratches, and mildew. Compared to the original YOLOv7, CCG-YOLOv7 improves precision, recall, and mean average precision by 2.1%, 1.6%, and 1.2%, respectively.

Ground-based cloud images can provide information on weather and cloud conditions, which are important for cloud monitoring and PV power generation forecasting. Prediction of short-time cloud movement from images is a major means of intra-hourly irradiation forecasting for solar power generation and is also important for precipitation forecasting. However, there is a lack of advanced and complete methods for cloud movement prediction from ground-based cloud images, and traditional techniques based on image processing and motion vector calculations have difficulty in predicting cloud morphological changes, which makes accurate prediction of cloud motion (especially nonlinear motion) very challenging. Therefore, this paper proposes CloudpredNet, a ground-based cloud ultra-short-term movement prediction model based on an "encoder-generator" architecture. This paper also proposes a loss function dedicated to the time series prediction of ground-based cloud images and combines the attention mechanism to train the model. The model is validated on a publicly available dataset, and it is demonstrated that it has good performance in all metrics of cloud image generation for the next 10 minutes.