Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+
[Objective]The picking of famous and high-quality tea is a crucial link in the tea industry. Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as la...
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Editorial Office of Smart Agriculture
2024-09-01
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| Series: | 智慧农业 |
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| Online Access: | https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202403016 |
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| author | HU Chengxi TAN Lixin WANG Wenyin SONG Min |
| author_facet | HU Chengxi TAN Lixin WANG Wenyin SONG Min |
| author_sort | HU Chengxi |
| collection | DOAJ |
| description | [Objective]The picking of famous and high-quality tea is a crucial link in the tea industry. Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points.[Methods]The primary technical innovation resided in the amalgamation of a lightweight network architecture, MobilenetV2, with an attention mechanism known as efficient channel attention network (ECANet), alongside optimization modules including atrous spatial pyramid pooling (ASPP). Initially, MobilenetV2 was employed as the feature extractor, substituting traditional convolution operations with depth wise separable convolutions. This led to a notable reduction in the model's parameter count and expedited the model training process. Subsequently, the innovative fusion of ECANet and ASPP modules constituted the ECA_ASPP module, with the intention of bolstering the model's capacity for fusing multi-scale features, especially pertinent to the intricate recognition of tea shoots. This fusion strategy facilitated the model's capability to capture more nuanced features of delicate shoots, thereby augmenting segmentation accuracy. The specific implementation steps entailed the feeding of image inputs through the improved network, whereupon MobilenetV2 was utilized to extract both shallow and deep features. Deep features were then fused via the ECA_ASPP module for the purpose of multi-scale feature integration, reinforcing the model's resilience to intricate backgrounds and variations in tea shoot morphology. Conversely, shallow features proceeded directly to the decoding stage, undergoing channel reduction processing before being integrated with upsampled deep features. This divide-and-conquer strategy effectively harnessed the benefits of features at differing levels of abstraction and, furthermore, heightened the model's recognition performance through meticulous feature fusion. Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points.[Results and Discussions]The experimental outcomes indicated that the enhanced DeepLabV3+ model had achieved an average Intersection over Union (IoU) of 93.71% and an average pixel accuracy of 97.25% on the dataset of tea shoots. Compared to the original model based on Xception, there was a substantial decrease in the parameter count from 54.714 million to a mere 5.818 million, effectively accomplishing a significant lightweight redesign of the model. Further comparisons with other prevalent semantic segmentation networks revealed that the improved model exhibited remarkable advantages concerning pivotal metrics such as the number of parameters, training duration, and average IoU, highlighting its efficacy and precision in the domain of tea shoot recognition. This considerable decreased in parameter numbers not only facilitated a more resource-economical deployment but also led to abbreviated training periods, rendering the model highly suitable for real-time implementations amidst tea garden ecosystems. The elevated mean IoU and pixel accuracy attested to the model's capacity for precise demarcation and identification of tea shoots, even amidst intricate and varied datasets, demonstrating resilience and adaptability in pragmatic contexts.[Conclusions]This study effectively implements an efficient and accurate tea shoot recognition method through targeted model improvements and optimizations, furnishing crucial technical support for the practical application of intelligent tea picking robots. The introduction of lightweight DeepLabV3+ not only substantially enhances recognition speed and segmentation accuracy, but also mitigates hardware requirements, thereby promoting the practical application of intelligent picking technology in the tea industry. |
| format | Article |
| id | doaj-art-09a70d4b1f6b44278ac77d29d217b638 |
| institution | OA Journals |
| issn | 2096-8094 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj-art-09a70d4b1f6b44278ac77d29d217b6382025-08-20T01:49:15ZengEditorial Office of Smart Agriculture智慧农业2096-80942024-09-016511912710.12133/j.smartag.SA202403016SA202403016Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+HU Chengxi0TAN Lixin1WANG Wenyin2SONG Min3College of Information and Intelligence, Hunan Agricultural University, Changsha410125, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha410125, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha410125, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha410125, China[Objective]The picking of famous and high-quality tea is a crucial link in the tea industry. Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points.[Methods]The primary technical innovation resided in the amalgamation of a lightweight network architecture, MobilenetV2, with an attention mechanism known as efficient channel attention network (ECANet), alongside optimization modules including atrous spatial pyramid pooling (ASPP). Initially, MobilenetV2 was employed as the feature extractor, substituting traditional convolution operations with depth wise separable convolutions. This led to a notable reduction in the model's parameter count and expedited the model training process. Subsequently, the innovative fusion of ECANet and ASPP modules constituted the ECA_ASPP module, with the intention of bolstering the model's capacity for fusing multi-scale features, especially pertinent to the intricate recognition of tea shoots. This fusion strategy facilitated the model's capability to capture more nuanced features of delicate shoots, thereby augmenting segmentation accuracy. The specific implementation steps entailed the feeding of image inputs through the improved network, whereupon MobilenetV2 was utilized to extract both shallow and deep features. Deep features were then fused via the ECA_ASPP module for the purpose of multi-scale feature integration, reinforcing the model's resilience to intricate backgrounds and variations in tea shoot morphology. Conversely, shallow features proceeded directly to the decoding stage, undergoing channel reduction processing before being integrated with upsampled deep features. This divide-and-conquer strategy effectively harnessed the benefits of features at differing levels of abstraction and, furthermore, heightened the model's recognition performance through meticulous feature fusion. Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points.[Results and Discussions]The experimental outcomes indicated that the enhanced DeepLabV3+ model had achieved an average Intersection over Union (IoU) of 93.71% and an average pixel accuracy of 97.25% on the dataset of tea shoots. Compared to the original model based on Xception, there was a substantial decrease in the parameter count from 54.714 million to a mere 5.818 million, effectively accomplishing a significant lightweight redesign of the model. Further comparisons with other prevalent semantic segmentation networks revealed that the improved model exhibited remarkable advantages concerning pivotal metrics such as the number of parameters, training duration, and average IoU, highlighting its efficacy and precision in the domain of tea shoot recognition. This considerable decreased in parameter numbers not only facilitated a more resource-economical deployment but also led to abbreviated training periods, rendering the model highly suitable for real-time implementations amidst tea garden ecosystems. The elevated mean IoU and pixel accuracy attested to the model's capacity for precise demarcation and identification of tea shoots, even amidst intricate and varied datasets, demonstrating resilience and adaptability in pragmatic contexts.[Conclusions]This study effectively implements an efficient and accurate tea shoot recognition method through targeted model improvements and optimizations, furnishing crucial technical support for the practical application of intelligent tea picking robots. The introduction of lightweight DeepLabV3+ not only substantially enhances recognition speed and segmentation accuracy, but also mitigates hardware requirements, thereby promoting the practical application of intelligent picking technology in the tea industry.https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202403016lightweight modeldeeplabv3+attention mechanismtender tea budsecanetfamous quality teaaspp |
| spellingShingle | HU Chengxi TAN Lixin WANG Wenyin SONG Min Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ 智慧农业 lightweight model deeplabv3+ attention mechanism tender tea buds ecanet famous quality tea aspp |
| title | Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ |
| title_full | Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ |
| title_fullStr | Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ |
| title_full_unstemmed | Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ |
| title_short | Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ |
| title_sort | lightweight tea shoot picking point recognition model based on improved deeplabv3 |
| topic | lightweight model deeplabv3+ attention mechanism tender tea buds ecanet famous quality tea aspp |
| url | https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202403016 |
| work_keys_str_mv | AT huchengxi lightweightteashootpickingpointrecognitionmodelbasedonimproveddeeplabv3 AT tanlixin lightweightteashootpickingpointrecognitionmodelbasedonimproveddeeplabv3 AT wangwenyin lightweightteashootpickingpointrecognitionmodelbasedonimproveddeeplabv3 AT songmin lightweightteashootpickingpointrecognitionmodelbasedonimproveddeeplabv3 |