| 1 | Analysis on virus resistance and fruit quality for T4 generation of transgenic papaya显示文摘Molecular biological characterization,fruit char-acters,and nutrients were analyzed for T4 generation of transgenic papaya.All transgenic papaya plants with the mutated replicase(RP)gene from papaya ringspot virus(PRSV)showed high resistance or immunity against PRSV in the field.The RP transgene can be steadily inherited to,and expressed at RNA level,the progenies.The growth char-acteristics of transgenic papaya were much better than non-transgenic papaya in the field.The non-transgenic papaya seedlings began to show typical symptoms caused by PRSV after being inoculated with PRSV.They died quickly and never grew to produce fruit.The adult trees developed yellow leaves and produced smaller fruits and were doomed to a slow death after some time,while most of transgenic papaya plants(about 91.8%)did not show any symptoms caused by PRSV,and produced more,bigger,and high quality fruits.Compared with non-transgenic plants,the fresh fruit length of T4 gen-eration of transgenic papaya increased 2.6%-5%,and the diameter decreased 0.6%-1.5%.The flesh thickness of fresh fruit increased 12%-15%,which made it fitter for eating.Although the fresh fruit quality changed,there was no sig-nificant difference between transgenic and non-transgenic papaya.The quality characteristics of dry fruit including the contents of water,lipid,N,protein,reduced sugar,vitamin A,vitamin C,and carotene in the T4 generation of transgenic papaya were all the same as their non-transgenic parents.This means that transgenic plants and non-transgenic plants are substantially equivalent,and the transgene has no effect on dry fruit quality.In this study,we found that vitamin A and vitamin C in red-fleshed papaya were 1.4-1.8 and 1.78-2.07 times more than the yellow-fleshed ones,respectively,while N and protein were only 84.2%-92.1%and 82.1%-98.9%of the yellow-fleshed ones. | WEI Xiangdong LAN Congyu LU Zhijing YE Changming | 2007 | Frontiers in Biology2007，2，3： | 1 |
| 2 | Detection of the foreign object positions in agricultural soils using Mask-RCNN显示文摘Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils. | Yuanhong Li Chaofeng Wang Congyue Wang Xiaoling Deng Zuoxi Zhao Shengde Chen Yubin Lan | 2023 | International Journal of Agricultural and Biological Engineering2023，16，1： | 0 |
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