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| 1 | Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning显示文摘Strawberry is one of the popular fruits with numerous nutrients.The ripeness of this fruits was estimated using the hyperspectral imaging(HSI)system in field and laboratory conditions in this study.Strawberry at early ripe and ripe stageswere collected HSI data,coveredwavelength ranges from370 to 1015 nm.Spectral featurewavelengths were selected using the sequential feature selection(SFS)algorithm.Two wavelengths selected for field(530 and 604 nm)and laboratory(528 and 715 nm)samples,respectively.Then,reliability of such spectral featureswas validated based on support vectormachine(SVM)classifier.Performance of SVMclassification models had good resultswith receiver operating characteristic values for samples under both field and laboratory conditions higher than 0.95.Meanwhile,the spatial feature images were extracted from the spectral feature wavelength and the first three principal components for laboratory samples.Pretrained AlexNet convolutional neural network(CNN)was used to classify the early ripe and ripe strawberry samples,which obtained the accuracy of 98.6%for test dataset.The above results indicated real-time HSI system was promising for estimating strawberry ripeness under field and laboratory conditions,which could be a potential application technique for evaluating the harvesting time management for farmers and producers. | Zongmei Gao Yuanyuan Shao Guantao Xuan Yongxian Wang Yi Liu Xiang Han | 2020 | Artificial Intelligence in Agriculture2020，，1： | 6 |
| 2 | Determination of damaged wheat kernels with hyperspectral imaging analysis显示文摘Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels.The spectral information was extracted from damaged wheat kernels and healthy kernels samples.The effective wavelengths were obtained from spectral of 865-1711 nm by X-loadings of principal component analysis(PCA)and successive projection algorithm(SPA)method,respectively.Partial least square method(PLS)and least square-support vector machine(LS-SVM)were then used to build classification models on full spectral data and effective wavelengths dataset,respectively.The results showed that the classification accuracy of every LS-SVM model was the best,being 100%.While the accuracy of the PLS model was slightly lower,still over 97%.The confusion matrix showed that several damaged wheat kernels samples were misclassified as healthy samples,while all healthy samples were correctly classified.The overall results indicated that hyperspectral imaging could be used for discriminating the damaged wheat kernels and could provide a reference for detecting other grain kernels grading degrees.Further,this study can provide a research basis for the development of online or portable detectors on grain damaged kernels recognition,which will be beneficial for grain grading or post-harvest quality processing of other grains. | Yuanyuan Shao Chong Gao Guantao Xuan Xuemei Gao Youqing Chen Zhichao Hu | 2020 | International Journal of Agricultural and Biological Engineering2020，13，5： | 2 |
| 3 | Simulation and experiment of a transplanting mechanism for sweet potato seedlings with ‘boat-bottom’ transplanting trajectory显示文摘Planting on mulched soil is the main cultivation mode for sweet potato slips in spring in northern China.The‘boatbottom’planting method can bring high quantity and similar size for sweet potatoes.In this study,a mechanism for transplanting sweet potato seedlings on mulched was proposed,which could realize‘boat-bottom’transplanting trajectory and improve the planting efficiency.The virtual prototype model of the transplanting mechanism was constructed using ADAMS software,a soil particle model and a flexible body model of sweet potato seedling were established using EDEM software,and then a joint simulation was carried out to find out the interaction of mechanism,seedling and soil.By simulation,the trajectory of the clamping point,the sweet potato seedling posture,soil particles,pits,and relative force were all analyzed,which verified the rationality of the transplanting mechanism design.Finally,three kinds of sweet potato seedlings,watermelon red,Yan-25,and purple sweet potato seedlings were selected,and transplanting experiments were carried out on the mulch to verify that the theoretical trajectory,the simulated trajectory,and the actual trajectory were consistent.The transplanting success rates of the three kinds of sweet potato seedlings were all above 85%,indicating that different varieties of sweet potato seedlings had little effect on the transplanting effect of the mechanism,which reflected that the transplanting mechanism had good practicability and adaptability. | Yuanyuan Shao Hongdong Zhang Guantao Xuan Tao Zhang Xianlu Guan Fuhui Wang | 2023 | International Journal of Agricultural and Biological Engineering2023，16，3： | 1 |
| 4 | Development of a multi-adaptive feeding device for automated plug seedling transplanter显示文摘In order to improve the adaptability of the seedling feeding devices to different plug trays for various seedlings,a multi-adaptive plug seedling feeding device for automatic field transplantation was developed.Its efficiency and adaptability have been improved using a seedling delivery device equipped with a row of end-effectors,which were provided with continuous plug seedlings by a compact seedling tray conveyor carrying out a reciprocating action.Each operation for the seedling feeding device could work automatically through a control system.Three categories of plug seedlings,cucumber,pepper and tomato seedlings were tested.The results of the performance test for the prototype demonstrated that the best performance emerged in the cucumber seedlings in 50-cell trays with the highest success rate of 88%at the feeding frequency of 6 groups/min.The lowest success rate was 73.33%for the tomato seedlings in 105-cell trays at the feeding frequency of 12 groups/min.It can be concluded that the automated feeding device developed in this study could be used for various plug seedlings as a result of its strong adaptability. | Yuanyuan Shao Xiang Han Guantao Xuan Yi Liu Chong Gao Gongpu Wang Zhichao Hu | 2021 | International Journal of Agricultural and Biological Engineering2021，14，2： | 0 |
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