/pdf/production-planning-and-control-54igdcohhl.pdf

Production Planning and Control

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Perturbation observer based fractional-order sliding-mode controller for MPPT of grid-connected PV inverters: Design and real-time implementation

Ripeness classification is one of the most challenging tasks in the postharvest management of mulberry fruit. The risks of microbial contamination and human error in manual sorting are significant; it may result in quality degradation and wasting of processed products. Due to advanced developments in computer vision and machine learning, automated sorting became possible. This study presents the results of developing and testing a computer vision-based application using convolutional neural networks (CNNs) for the classification of mulberry fruit ripening stages. To reduce the training cost and improve the accuracy of classification, transfer learning was used to fine-tune the CNN models. The CNN models in the test include DenseNet, Inception-v3, ResNet-18, ResNet-50, and AlexNet. Transfer learning was used to fine-tune the models and improve the accuracy of classification. The AlexNet and ResNet-18 networks exhibited the best performance with 98.32% and 98.65% overall accuracy for classifying the ripeness of white and black mulberries, respectively. Moreover, the performance of the models did not change when the data sets of both genotypes were mixed. The ResNet-18 was able to classify both genotype and ripeness from 600 fruit images in 2.36 min with an overall accuracy of 98.03%, which was superior to other architectures. It indicates that the model could be used for precise classification of the ripening stages of mulberries and other horticultural products, as a part of an automated sorting system.

/pdf/detection-of-mulberry-ripeness-stages-using-deep-learning-4llgl7ca5i.pdf

Detection of Mulberry Ripeness Stages Using Deep Learning Models

Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

In order to solve the current fruit surface disease detection algorithm’s problems of low accuracy, slow speed and heavy workload of quality classification, this paper takes apple, peach, orange, and pear as the research objects and proposes a model based on Mask R-CNN for detecting disease spots on the surface of fruits which accurately detects the defects on the surface of the fruit after the picking robot recognizes and locates the fruit. By adding a bottom-up horizontal connection path, the feature pyramid (FPN) structure of Mask R-CNN is improved to enhance the fusion of high and low-level features. Experimental research shows that the improved Mask R-CNN algorithm has a detection accuracy of more than 95% for the four kinds of fruit surface lesions, and the detection speed reaches 2.6 frames per second when using GPU, which is significantly better than Fast R-CNN and SSD algorithms and has good detection performance and robustness.

Research on Detection Technology of Various Fruit Disease Spots Based on Mask R-CNN

In order to improve the accuracy of prediction for the temperature and humidity of the northern greenhouse, this paper proposes a model to predict the temperature and humidity of a greenhouse based on improved LM-RBF. The input data of the model were measured in a greenhouse in Tianjin in March. This model uses the inside and outside meteorological data of the greenhouse as input, and the temperature and humidity in a greenhouse as output. The higher prediction accuracy is obtained by the experimental results, which proved the feasibility of this scheme. This model can be used to forecast the temperature and humidity of a greenhouse and guide the control of the temperature and humidity of a greenhouse.

The Prediction of Greenhouse Temperature and Humidity Based on LM-RBF Network

For the problems of uneven cluster distribution and excessive route energy consumption, which are caused by the clustering of low energy adaptive clustering algorithm (LEACH) in wireless sensor networks, an algorithm based on the clustering of double cluster heads and the data fusion mechanism of information entropy are proposed: In the cluster head selection algorithm, Select two levels of cluster heads in a well-divided cluster, two cluster heads perform different duties, this can be better to share the energy consumption, extend the network life cycle. Cluster head nodes use information entropy for classification and fusion, making the fusion results more accurate and data transmission more efficient. The algorithm is a good way to balance the energy consumption of nodes of a wireless sensor network composed of static location nodes, and extend the survival time of the network.

Research on LEACH algorithm based on double cluster head cluster clustering and data fusion

In order to improve the accuracy of blast furnace hot metal temperature prediction in iron and steel enterprises, the Grey Relational Analysis and Support Vector Machine optimized by Genetic Algorithm Optimization method is proposed to solve this. Firstly, the main factors which influence the blast furnace hot metal temperature are determined by the Grey Relational Analysis, and then the prediction model is established by the support vector machine based on Genetic Algorithm Optimization. This method is compared with other prediction methods. The simulation result indicates GRA-GA-SVM have higher prediction accuracy. A new and more effective method is put forward for blast furnace hot metal temperature of prediction.

Study on prediction model of blast furnace hot metal temperature

With the development of image processing and machine vision, there are emerging fish species classification algorithms that use machine vision technology to identify specific scenes. In order to solve the problem of poor working environment and low efficiency of traditional artificial identification of fish species, this paper proposes a deep learning method combining SPP (spatial pyramid pooling) and DenseNet (dense neural network). The model is connects the spatial pyramid pooling layer in front of the full connection layer of DenseNet network. On the basis of maintaining good recognition accuracy of DenseNet, the integrated new network can increase scale invariance and reduce overfitting. Through the identification test of 6 kinds of freshwater fish, the classification accuracy rate reached 97%. Experimental results show that the new network SPP-Densenet can effectively identify species of freshwater fish images.

Study on Freshwater Fish Image Recognition Integrating SPP and DenseNet Network

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