Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model based on transfer learning with convolutional neural networks (CNNs). In the naturally growing vineyards, 195,120,135 samples of Grade I, Grade II, and Grade III red grapes were collected using a Canon EOS 550D camera, and a data set of 1800 samples was obtained using data enhancement technology. Then, the CNN transfer learning method was used to transfer the pre-trained AlexNet, VGG16, GoogleNet, InceptionV3, and ResNet50 network models on the ImageNet image dataset to the red grape image grading task. By comparing the classification performance of the CNN models of these five different network depths with fine-tuning, ResNet50 with a learning rate of 0.001 and a loop number of 10 was determined to be the best feature extractor for red grape images. Moreover, given the small number of red grape image samples in this study, different convolutional layer features output by the ResNet50 feature extractor were analyzed layer by layer to determine the effect of deep features extracted by each convolutional layer on Support Vector Machine (SVM) classification performance. This analysis helped to obtain a ResNet50 + SVM red grape external appearance grading model based on the optimal ResNet50 feature extraction strategy. Experimental data showed that the classification model constructed using the feature parameters extracted from the 10th node of the ResNet50 network achieved an accuracy rate of 95.08% for red grape grading. These research results provide a reference for the online grading of red grape clusters based on external appearance quality and have certain guiding significance for the quality and efficiency of grape industry circulation and production.

Crispness is a critical indicator for fruit texture evaluation, which is directly associated with fruit freshness and consumer preference. In this study, a new instrumental method was applied to synchronously collect mechanical-acoustic signals at high sampling rate of 51,200 Hz for “noisy” Korla pear in puncture. The mechanical-acoustic jagged analytic spectral were fused at the data-level to imitate human perception behavior. Three types of features totaling 26 were extracted from the fusion signals to establish four machine learning models. Among four models, the extreme gradient boosting (XGBoost) model exhibited the superior performance in crispness prediction. Furthermore, the Shapley additive explanations (SHAP) analysis was performed to interpret the XGBoost model. The 14 features with the positive impact outcome were then selected to improve the model. The explainable model achieved an R_P² value of 0.92, an RMSEP of 0.32, and an RPD of 3.66 with a higher accuracy, stability and reliability. Hence, the use of synchronous acquisition at high sampling rate, data-level fusion strategy and positive features selection can significantly enhance the crispness prediction performance of pear. Our proposed method can be applicable to other fruit and vegetables for instrumental crispness measurement.

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high‐yield and low‐energy‐consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self‐organizing capabilities of each node to automatically establish the entire network. In the large‐scale self‐organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP‐hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model based on transfer learning with convolutional neural networks (CNNs). Initially, the CNN transfer learning method was used to transfer the pre-trained AlexNet, VGG16, GoogleNet, InceptionV3, and ResNet50 network models on the ImageNet image dataset to the red grape image grading task. By comparing the classification performance of the CNN models of these five different network depths with fine-tuning, ResNet50 with a learning rate of 0.001 and a loop number of 10 was determined to be the best feature extractor for red grape images. Moreover, given the small number of red grape image samples in this study, different convolutional layer features output by the ResNet50 feature extractor were analyzed layer by layer to determine the effect of deep features extracted by each convolutional layer on SVM classification performance. This analysis helped to obtain a ResNet50+SVM red grape external appearance grading model based on the optimal ResNet50 feature extraction strategy. Experimental data showed that the classification model constructed using the feature parameters extracted from the 10th node of the ResNet50 network achieved an accuracy rate of 95.08% for red grape grading. These research results provide a reference for the online grading of red grape clusters based on external appearance quality and have certain guiding significance for the quality and efficiency of grape industry circulation and production.

Large scale wireless sensor network (LSWSN) was recently developed for grape growing industry. This technology can easily be tailored for grape planting such as environmental variables monitoring and grape health status monitoring. Since sensor nodes are low power devices with limited sensing capabilities, target coverage plays a crucial role in maximizing the coverage rate, while most researches for target coverage are related to a target coverage algorithm. Such a problem is important in enhancing monitoring efficiency. However, finding the optimal target coverage is an NP-hard problem. Hence, an improved immune shuffled frog leaping algorithm (IISFLA) is proposed in this paper for target coverage in LSWSNs in a vineyard facility. In this work, we first propose a novel formulation of the objective function to calculate the target coverage rate to satisfy the monitoring efficiency. The IISFLA scheme combines the merits of improve generator and immune operator, while maintaining the contention for the shuffled frog leaping algorithm. Comprehensive analysis and simulations are conducted to evaluate the performance improvement of IISFLA compared to methods based on ACO and GA. Simulation results demonstrate the superior performance of the proposed IISFLA in both the point coverage rate as well as fast convergence. An improved immune strategy is implemented to achieve better results and at the same time, avoid local optima.