It is vital to promote and optimize the technological innovation efficiency of new energy vehicle (NEV) enterprises for the green transformation of China’s automobile industry. However, China’s NEV enterprises still have problems such as insufficient research of technology and unreasonable innovative resource allocation. To improve the technological innovation efficiency of China’s NEV enterprises, the NEVs’ technological innovation process is divided into two stages: the research and development (R&D) stage and the achievement transformation stage in this research. Combining Tobit regression with data envelopment analysis (DEA), an evaluation framework of technological innovation efficiency of the NEV enterprises is constructed. Then, the innovation efficiency of 23 NEV listed enterprises from 2013 to 2018 is analyzed. The result reveals three findings. First, the overall technological innovation efficiency of NEV enterprises is low. Second, enterprises’ R&D efficiency is generally higher than the achievement transformation efficiency. Third, according to two-stage efficiency, 23 NEV enterprises are divided into four categories. For different types of enterprises, targeted guidance to improve innovation efficiency is proposed. This research provides a theoretical and practical basis for improving the innovation efficiency of NEV enterprises.

Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea.

Plant wearable sensors have shown exceptional promise in continuously monitoring plant health. However, the potential adverse effects of these sensors on plant growth remain unclear. This study systematically quantifies wearable sensors’ interference with plant growth using two ornamental species, Peperomia tetraphylla and Epipremnum aureum. We evaluated the impacts of four common disturbances—mechanical pressure, hindrance of gas exchange, hindrance of light acquisition, and mechanical constraint—on leaf growth. Our results indicated that the combination of light hindrance and mechanical constraint demonstrated the most significant interference. When the sensor weight was no greater than 0.6 g and the coverage was no greater than 5% of the leaf area, these four disturbances resulted in slight impacts on leaf growth. Additionally, we fabricated a minimally interfering wearable sensor capable of measuring the air temperature of the microclimate of the plant while maintaining plant growth. This research provides valuable insights into optimizing plant wearable sensors, balancing functionality with minimal plant interference.

This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than $5\times {10}^4 \mathrm{L}\mathrm{u}\mathrm{x}$, the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone.

The ripeness of tomatoes is crucial to determining their shelf life and quality. Most of the current methods for picking and sorting tomatoes take a long time, so this paper aims to design a device for sorting tomatoes based on force and bioelectrical impedance measurement. A force sensor installed on each of its four fingers may be used as an impedance measurement electrode. When picking tomatoes, the electrical impedance analysis circuit is first connected for pre-grasping. By applying a certain pre-tightening force, the FSR sensor on the end effector finger can be tightly attached to the tomato and establish an electric current pathway. Then, the electrical parameters of the tomato are measured to determine its maturity, and some of the electrical parameters are used for force monitoring compensation. Then, a force analysis is conducted to consider the resistance of the FSR under current stress. According to the principle of complex impedance circuit voltage division, the voltage signal on the tomato is determined. At the same time, the specific value of the grasping force at this time is determined based on the calibration of the pre-experiment and the compensation during the detection process, achieving real-time detection of the grasping force. The bioelectricity parameters of tomatoes can not only judge the ripeness of tomatoes, but also compensate for the force measurement stage to achieve more accurate non-destructive sorting. The experimental results showed that within 0.6 s of stable grasping, this system could complete tomato ripeness detection, improve the overall tomato sorting efficiency, and achieve 95% accuracy in identifying ripeness through impedance.

This paper proposes a method for localized damage detection in tomato, with the objective of enabling the detection of bruises prior to sorting. Bioimpedance spectroscopy technology is employed to assess the extent of localized damage in tomato. An equivalent circuit model is constructed, and the impedance spectroscopy data are obtained by developing a local damage measurement platform for tomatoes using a self-designed circular four-electrode BIS sensor. The electrical parameters are then extracted by fitting the constructed equivalent circuit model to the tomato data. Subsequently, we analyze the variation rules of the electrical parameters in different damage levels. To reduce the dimensionality of the features, including biological variables, fitted electrical parameters, and tomato ripeness, we employ Spearman feature selection. We then classify the reduced features by combining the advantages of the support vector machine and the artificial neural network. The results demonstrate that the designed circular four-electrode BIS sensor can non-destructively measure localized damage conditions in tomato. A localized damage measurement platform for tomatoes has been constructed using this sensor. A comparison of the impedance measurements obtained using the designed circular four-electrode BIS sensor with those obtained using a needle sensor proposed by previous scholars revealed that both sensors exhibited a decrease in impedance with increasing damage degree. This finding indicates that the designed circular four-electrode BIS sensor is an effective tool for characterizing damage conditions in tomatoes. The design of the tomato circular four-electrode BIS sensor is an effective means of characterizing tomato damage. The Spearman-SVM-ANN damage classification algorithm, based on the Spearman feature selection, effectively classified tomato damage with a 98.765% accuracy rate. The findings of this study provide a reference for the grading and transportation of tomatoes after harvest.