A red Fuji apple appearance grading method based on improved whale optimization algorithm and CNN

Objective: In order to improve the accuracy of machine vision technology in grading the appearance quality of red Fuji apples, a red Fuji apple appearance grading method based on improved whale optimization algorithm （WOA） and CNN is proposed. Methods: A red Fuji apple image database with different appearance quality levels was established, and the database images were preprocessed so as to improve the training effect and generalization ability of the model. The improved CNN-LSTM was designed as the weighted grey correlation method was used to compress the CNN convolution scale, in order to reduce redundant interference between features and improve the computational speed of the model. The improved whale optimization algorithm was used to optimize the hyperparameters configuration of CNN-LSTM, effectively reducing the impact of improper hyperparameter configuration on model classification results. Results: The simulation results showed that the proposed classification method had a higher accuracy, with classification accuracy and sensitivity improved by about 2.05% and 2.46%. Conclusion: The proposed method can effectively achieve the appearance grading of red Fuji apples

Mutation of Cellulose Synthase Gene Improves the Nutritive Value of Rice Straw

Rice straw is an important roughage resource for ruminants in many rice-producing countries. In this study, a rice brittle mutant (BM, mutation in OsCesA4, encoding cellulose synthase) and its wild type (WT) were employed to investigate the effects of a cellulose synthase gene mutation on rice straw morphological fractions, chemical composition, stem histological structure and in situ digestibility. The morphological fractions investigation showed that BM had a higher leaf sheath proportion (43.70% vs 38.21%, p0.05) was detected in neutral detergent fiber (NDFom) and ADL contents for both strains. Histological structure observation indicated that BM stems had fewer sclerenchyma cells and a thinner sclerenchyma cell wall than WT. The results of in situ digestion showed that BM had higher DM, NDFom, cellulose and hemicellulose disappearance at 24 or 48 h of incubation (p<0.05). The effective digestibility of BM rice straw DM and NDFom was greater than that of WT (31.4% vs 26.7% for DM, 29.1% vs 24.3% for NDFom, p<0.05), but the rate of digestion of the slowly digested fraction of BM rice straw DM and NDF was decreased. These results indicated that the mutation in the cellulose synthase gene could improve the nutritive value of rice straw for ruminants

Table1_A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.pdf

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient’s training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient’s condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.</p