Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
The tea green leafhopper, Empoasca vitis Gothe, is one of the most serious insect pests of tea plantations in China's Mainland. Over the past decades, this pest has been controlled mainly by spraying pesticides. I...The tea green leafhopper, Empoasca vitis Gothe, is one of the most serious insect pests of tea plantations in China's Mainland. Over the past decades, this pest has been controlled mainly by spraying pesticides. Insecticide applications not only have become less effective in controlling damage, but even more seriously, have caused high levels of toxic residues in teas, which ultimately threatens human health. Therefore, we should seek a safer biological control approach. In the present study, key components of tea shoot volatiles were identified and behaviorally tested as potential leafhopper attractants. The following 13 volatile compounds were identified from aeration samples of tea shoots using gas chromatography-mass spectrometry （GC-MS）： （E）-2-hexenal, （Z）-3-hexen-1- ol, （Z）-3-hexenyl acetate, 2-ethyl-1-hexanol, （E）-ocimene, linalool, nonanol, （Z）-butanoic acid, 3-hexenyl ester, decanal, tetradecane, β-caryophyllene, geraniol and hexadecane. In Y-tube olfactometer tests, the following individual compounds were identified： （E）-2- hexenal, （E）-ocimene, （Z）-3-hexenyl acetate and linalool, as well as two synthetic mixtures （called blend 1 and blend 2） elicited significant taxis, with blend 2 being the most attractive. Blend 1 included linalool, （Z）-3-hexen-l-ol and （E）-2-hexenal at a 1： 1：1 ratio, whereas blend 2 was a mixture of eight compounds at the same loading ratio： （E）-2-hexenal, （Z）- 3-hexen-l-ol, （Z）-3-hexenyl acetate, 2-penten-l-ol, （E）-2-pentenal, pentanol, hexanol and 1-penten-3-ol. In tea fields, the bud-green sticky board traps baited with blend 2, （E）-2- hexenal or hexane captured adults and nymphs of the leafhoppers, with blend 2 being the most attractive, foUowed by （E）-2-hexenal and hexane. Placing sticky traps baited with blend 2 or （E）-2-hexenal in the tea fields significantly reduced leathopper populations. Our results indicate that the bud-green sticky traps baited with tea shoot volatiles can provide a new tool for monitoring and managing the tea leafhopper.展开更多
Achieving high-efficiency and accurate detection of tea shoots in fields are essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algor...Achieving high-efficiency and accurate detection of tea shoots in fields are essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algorithm was proposed in this study. First, tea shoot images were collected and data augmentation was performed to increase sample diversity, and then a spatial pyramid pooling module was added to the YOLOv3 model to detect tea shoots. To simplify the tea shoot detection model and improve the detection speed, the channel pruning algorithm and layer pruning algorithm were used to compress the model. Finally, the model was fine-tuned to restore its accuracy, and achieve the fast and accurate detection of tea shoots. The test results demonstrated that the number of parameters, model size, and inference time of the tea shoot detection model after compression reduced by 96.82%, 96.81%, and 59.62%, respectively, whereas the mean average precision of the model was only 0.40% lower than that of the original model. In the field test, the compressed model was deployed on a Jetson Xavier NX to conduct the detection of tea shoots. The experimental results demonstrated that the detection speed of the compressed model was 15.9 fps, which was 3.18 times that of the original model. All the results indicate that the proposed method could be deployed on tea harvesting robots with low computing power to achieve high efficiency and accurate detection.展开更多
An HPLC method was used to analyze the contents ana variation of IAA, GA3, ABA and ZT at five stages around the tea shoot germinating in spring. The contents of GA3 and ABA had a top value during the winter and decrea...An HPLC method was used to analyze the contents ana variation of IAA, GA3, ABA and ZT at five stages around the tea shoot germinating in spring. The contents of GA3 and ABA had a top value during the winter and decreased with the growth of tea shoots, while the contents of IAA and ZT had a low value during the winter and increased quickly at the beginning of shoot growth, but soon afterwards increased slowly or decreased a little. The ratio of hormones was closely related to the growth of tea plant. The study indicated that the ratios of GA3 to ABA and IAA to ABA were at low values during the winter and went up with the shoot germinating. When the activity of roots was weak, the ratio of ZT to IAA had a top value, but went down gradually with luxuriant activity of roots. The ratio of GA3 to ZT had a certin relativity with the shoot germinating, which was at a top value during the winter but went down suddenly at the beginning of shoot germinating.展开更多
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
文摘The tea green leafhopper, Empoasca vitis Gothe, is one of the most serious insect pests of tea plantations in China's Mainland. Over the past decades, this pest has been controlled mainly by spraying pesticides. Insecticide applications not only have become less effective in controlling damage, but even more seriously, have caused high levels of toxic residues in teas, which ultimately threatens human health. Therefore, we should seek a safer biological control approach. In the present study, key components of tea shoot volatiles were identified and behaviorally tested as potential leafhopper attractants. The following 13 volatile compounds were identified from aeration samples of tea shoots using gas chromatography-mass spectrometry （GC-MS）： （E）-2-hexenal, （Z）-3-hexen-1- ol, （Z）-3-hexenyl acetate, 2-ethyl-1-hexanol, （E）-ocimene, linalool, nonanol, （Z）-butanoic acid, 3-hexenyl ester, decanal, tetradecane, β-caryophyllene, geraniol and hexadecane. In Y-tube olfactometer tests, the following individual compounds were identified： （E）-2- hexenal, （E）-ocimene, （Z）-3-hexenyl acetate and linalool, as well as two synthetic mixtures （called blend 1 and blend 2） elicited significant taxis, with blend 2 being the most attractive. Blend 1 included linalool, （Z）-3-hexen-l-ol and （E）-2-hexenal at a 1： 1：1 ratio, whereas blend 2 was a mixture of eight compounds at the same loading ratio： （E）-2-hexenal, （Z）- 3-hexen-l-ol, （Z）-3-hexenyl acetate, 2-penten-l-ol, （E）-2-pentenal, pentanol, hexanol and 1-penten-3-ol. In tea fields, the bud-green sticky board traps baited with blend 2, （E）-2- hexenal or hexane captured adults and nymphs of the leafhoppers, with blend 2 being the most attractive, foUowed by （E）-2-hexenal and hexane. Placing sticky traps baited with blend 2 or （E）-2-hexenal in the tea fields significantly reduced leathopper populations. Our results indicate that the bud-green sticky traps baited with tea shoot volatiles can provide a new tool for monitoring and managing the tea leafhopper.
基金This work was financially supported by the China Agriculture Research System of Ministry of Finance and Ministry of Agriculture and Rural Affairs and the National Natural Science Foundation of China(Grant No.51975537).
文摘Achieving high-efficiency and accurate detection of tea shoots in fields are essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algorithm was proposed in this study. First, tea shoot images were collected and data augmentation was performed to increase sample diversity, and then a spatial pyramid pooling module was added to the YOLOv3 model to detect tea shoots. To simplify the tea shoot detection model and improve the detection speed, the channel pruning algorithm and layer pruning algorithm were used to compress the model. Finally, the model was fine-tuned to restore its accuracy, and achieve the fast and accurate detection of tea shoots. The test results demonstrated that the number of parameters, model size, and inference time of the tea shoot detection model after compression reduced by 96.82%, 96.81%, and 59.62%, respectively, whereas the mean average precision of the model was only 0.40% lower than that of the original model. In the field test, the compressed model was deployed on a Jetson Xavier NX to conduct the detection of tea shoots. The experimental results demonstrated that the detection speed of the compressed model was 15.9 fps, which was 3.18 times that of the original model. All the results indicate that the proposed method could be deployed on tea harvesting robots with low computing power to achieve high efficiency and accurate detection.
文摘An HPLC method was used to analyze the contents ana variation of IAA, GA3, ABA and ZT at five stages around the tea shoot germinating in spring. The contents of GA3 and ABA had a top value during the winter and decreased with the growth of tea shoots, while the contents of IAA and ZT had a low value during the winter and increased quickly at the beginning of shoot growth, but soon afterwards increased slowly or decreased a little. The ratio of hormones was closely related to the growth of tea plant. The study indicated that the ratios of GA3 to ABA and IAA to ABA were at low values during the winter and went up with the shoot germinating. When the activity of roots was weak, the ratio of ZT to IAA had a top value, but went down gradually with luxuriant activity of roots. The ratio of GA3 to ZT had a certin relativity with the shoot germinating, which was at a top value during the winter but went down suddenly at the beginning of shoot germinating.
