Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-08-30 Cooperative journals: 《智慧农业(中英文)》
Abstract: [Objective] The identification of agricultural activities plays a crucial role for greenhouse vegetables production, particularly in the precise management of cucumber cultivation. By monitoring and analyzing the timing and procedures of agricultural operations, effective guidance can be provided for agricultural production, leading to increased crop yield and quality. However, in practical applications, the recognition of agricultural activities in cucumber cultivation faces significant challenges. The complex and ever-changing growing environment of cucumbers, including dense foliage and internal facility structures that may obstruct visibility, poses difficulties in recognizing agricultural activities. Additionally, agricultural tasks involve various stages such as planting, irrigation, fertilization, and pruning, each with specific operational intricacies and skill requirements. This requires the recognition system to accurately capture the characteristics of various complex movements to ensure the accuracy and reliability of the entire recognition process. To address the complex challenges, an innovative algorithm: SlowFast-SMC-ECA (SlowFast-Spatio-Temporal Excitation, Channel Excitation, Motion Excitation-Efficient Channel Attention) was proposed for the recognition of agricultural activity behaviors in cucumber cultivation within facilities. [Methods] This algorithm represents a significant enhancement to the traditional SlowFast model, with the goal of more accurately capturing hand motion features and crucial dynamic information in agricultural activities. The fundamental concept of the SlowFast model involved processing video streams through two distinct pathways: the Slow Pathway concentrated on capturing spatial detail information, while the Fast Pathway emphasized capturing temporal changes in rapid movements. To further improve information exchange between the Slow and Fast pathways, lateral connections were incorporated at each stage. Building upon this foundation, the study introduced innovative enhancements to both pathways, improving the overall performance of the model. In the Fast Pathway, a multi-path residual network (SMC) concept was introduced, incorporating convolutional layers between different channels to strengthen temporal interconnectivity. This design enabled the algorithm to sensitively detect subtle temporal variations in rapid movements, thereby enhancing the recognition capability for swift agricultural actions. Meanwhile, in the Slow Pathway, the traditional residual block was replaced with the ECA-Res structure, integrating an effective channel attention mechanism (ECA) to improve the model’s capacity to capture channel information. The adaptive adjustment of channel weights by the ECA-Res structure enriched feature expression and differentiation, enhancing the model’s understanding and grasp of key spatial information in agricultural activities. Furthermore, to address the challenge of class imbalance in practical scenarios, a balanced loss function (Smoothing Loss) was developed. By introducing regularization coefficients, this loss function could automatically adjust the weights of different categories during training, effectively mitigating the impact of class imbalance and ensuring improved recognition performance across all categories. [Results and Discussions] The experimental results significantly demonstrated the outstanding performance of the improved SlowFast- SMC-ECA model on a specially constructed agricultural activity dataset. Specifically, the model achieved an average recognition accuracy of 80.47%, representing an improvement of approximately 3.5% compared to the original SlowFast model. This achievement highlighted the effectiveness of the proposed improvements. Further ablation studies revealed that replacing traditional residual blocks with the multi-path residual network (SMC) and ECA-Res structures in the second and third stages of the SlowFast model leads to superior results. This highlighted that the improvements made to the Fast Pathway and Slow Pathway played a crucial role in enhancing the model’s ability to capture details of agricultural activities. Additional ablation studies also confirmed the significant impact of these two improvements on improving the accuracy of agricultural activity recognition. Compared to existing algorithms, the improved SlowFast-SMC-ECA model exhibited a clear advantage in prediction accuracy. This not only validated the potential application of the proposed model in agricultural activity recognition but also provided strong technical support for the advancement of precision agriculture technology. In conclusion, through careful refinement and optimization of the SlowFast model, it was successfully enhanced the model’s recognition capabilities in complex agricultural scenarios, contributing valuable technological advancements to precision management in greenhouse cucumber cultivation. [Conclusions] By introducing advanced recognition technologies and intelligent algorithms, this study enhances the accuracy and efficiency of monitoring agricultural activities, assists farmers and agricultural experts in managing and guiding the operational processes within planting facilities more efficiently. Moreover, the research outcomes are of immense value in improving the traceability system for agricultural product quality and safety, ensuring the reliability and transparency of agricultural product quality.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-07-17 Cooperative journals: 《智慧农业(中英文)》
Abstract: [Objective] The identification of agricultural activities plays a crucial role for greenhouse vegetables production, particularly in the precise management of cucumber cultivation. By monitoring and analyzing the timing and procedures of agricultural operations, effective guidance can be provided for agricultural production, leading to increased crop yield and quality. However, in practical applications, the recognition of agricultural activities in cucumber cultivation faces significant challenges. The complex and ever-changing growing environment of cucumbers, including dense foliage and internal facility structures that may obstruct visibility, poses difficulties in recognizing agricultural activities. Additionally, agricultural tasks involve various stages such as planting, irrigation, fertilization, and pruning, each with specific operational intricacies and skill requirements. This requires the recognition system to accurately capture the characteristics of various complex movements to ensure the accuracy and reliability of the entire recognition process. To address the complex challenges, an innovative algorithm: SlowFast-SMC-ECA (SlowFast-Spatio-Temporal Excitation, Channel Excitation, Motion Excitation-Efficient Channel Attention) was proposed for the recognition of agricultural activity behaviors in cucumber cultivation within facilities. [Methods] This algorithm represents a significant enhancement to the traditional SlowFast model, with the goal of more accurately capturing hand motion features and crucial dynamic information in agricultural activities. The fundamental concept of the SlowFast model involved processing video streams through two distinct pathways: the Slow Pathway concentrated on capturing spatial detail information, while the Fast Pathway emphasized capturing temporal changes in rapid movements. To further improve information exchange between the Slow and Fast pathways, lateral connections were incorporated at each stage. Building upon this foundation, the study introduced innovative enhancements to both pathways, improving the overall performance of the model. In the Fast Pathway, a multi-path residual network (SMC) concept was introduced, incorporating convolutional layers between different channels to strengthen temporal interconnectivity. This design enabled the algorithm to sensitively detect subtle temporal variations in rapid movements, thereby enhancing the recognition capability for swift agricultural actions. Meanwhile, in the Slow Pathway, the traditional residual block was replaced with the ECA-Res structure, integrating an effective channel attention mechanism (ECA) to improve the model’s capacity to capture channel information. The adaptive adjustment of channel weights by the ECA-Res structure enriched feature expression and differentiation, enhancing the model’s understanding and grasp of key spatial information in agricultural activities. Furthermore, to address the challenge of class imbalance in practical scenarios, a balanced loss function (Smoothing Loss) was developed. By introducing regularization coefficients, this loss function could automatically adjust the weights of different categories during training, effectively mitigating the impact of class imbalance and ensuring improved recognition performance across all categories. [Results and Discussions] The experimental results significantly demonstrated the outstanding performance of the improved SlowFast- SMC-ECA model on a specially constructed agricultural activity dataset. Specifically, the model achieved an average recognition accuracy of 80.47%, representing an improvement of approximately 3.5% compared to the original SlowFast model. This achievement highlighted the effectiveness of the proposed improvements. Further ablation studies revealed that replacing traditional residual blocks with the multi-path residual network (SMC) and ECA-Res structures in the second and third stages of the SlowFast model leads to superior results. This highlighted that the improvements made to the Fast Pathway and Slow Pathway played a crucial role in enhancing the model’s ability to capture details of agricultural activities. Additional ablation studies also confirmed the significant impact of these two improvements on improving the accuracy of agricultural activity recognition. Compared to existing algorithms, the improved SlowFast-SMC-ECA model exhibited a clear advantage in prediction accuracy. This not only validated the potential application of the proposed model in agricultural activity recognition but also provided strong technical support for the advancement of precision agriculture technology. In conclusion, through careful refinement and optimization of the SlowFast model, it was successfully enhanced the model’s recognition capabilities in complex agricultural scenarios, contributing valuable technological advancements to precision management in greenhouse cucumber cultivation. [Conclusions] By introducing advanced recognition technologies and intelligent algorithms, this study enhances the accuracy and efficiency of monitoring agricultural activities, assists farmers and agricultural experts in managing and guiding the operational processes within planting facilities more efficiently. Moreover, the research outcomes are of immense value in improving the traceability system for agricultural product quality and safety, ensuring the reliability and transparency of agricultural product quality.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-06-17 Cooperative journals: 《智慧农业(中英文)》
Abstract: Objective Currently, the lack of computerized systems to monitor the quality of cabbage transplants is a notable shortcoming in the agricultural industry, where transplanting operations play a crucial role in determining the overall yield and quality of the crop. To address this problem, a lightweight and efficient algorithm was developed to monitor the status of cabbage transplants in a natural environment. Methods First, the cabbage image dataset was established, the cabbage images in the natural environment were collected, the collected image data were filtered and the transplanting status of the cabbage was set as normal seedling (upright and intact seedling), buried seedling (whose stems and leaves were buried by the soil) and exposed seedling (whose roots were exposed), and the dataset was manually categorized and labelled using a graphical image annotation tool (LabelImg) so that corresponding XML files could be generated. And the dataset was pre-processed with data enhancement methods such as flipping, cropping, blurring and random brightness mode to eliminate the scale and position differences between the cabbages in the test and training sets and to improve the imbalance of the data. Then, a cabbage transplantation state detection model based on YOLOv8s (You Only Look Once Version 8s) was designed. To address the problem that light and soil have a large influence on the identification of the transplantation state of cabbage in the natural environment, a multi-scale attention mechanism was embedded to increase the number of features in the model, and a multi-scale attention mechanism was embedded to increase the number of features in the model. Embedding the multi-scale attention mechanism to increase the algorithm’s attention to the target region and improve the network’s attention to target features at different scales, so as to improve the model’s detection efficiency and target recognition accuracy, and reduce the leakage rate; by combining with deformable convolution, more useful target information was captured to improve the model’s target recognition and convergence effect, and the model complexity increased by C3-layer convolution was reduced, which further reduced the model complexity. Due to the unsatisfactory localization effect of the algorithm, the focal extended intersection over union loss (Focal-EIoU Loss) was introduced to solve the problem of violent oscillation of the loss value caused by low-quality samples, and the influence weight of high-quality samples on the loss value was increased while the influence of low-quality samples was suppressed, so as to improve the convergence speed and localization accuracy of the algorithm. Results and Discussions Eventually, the algorithm was put through a stringent testing phase, yielding a remarkable recognition accuracy of 96.2% for the task of cabbage transplantation state. This was an improvement of 2.8% over the widely used YOLOv8s. Moreover, when benchmarked against other prominent target detection models, the algorithm emerged as a clear winner. It showcased a notable enhancement of 3% and 8.9% in detection performance compared to YOLOv3-tiny. Simultaneously, it also managed to achieve a 3.7% increase in the recall rate, a metric that measured the efficiency of the algorithm in identifying actual targets among false positives. On a comparative note, the algorithm outperformed YOLOv5 in terms of recall rate by 1.1%, 2% and 1.5%, respectively. When pitted against the robust faster region-based convolutional neural network (Faster R-CNN), the algorithm demonstrated a significant boost in recall rate by 20.8% and 11.4%, resulting in an overall improvement of 13%. A similar trend was observed when the algorithm was compared to the single shot multibox detector (SSD) model, with a notable 9.4% and 6.1% improvement in recall rate. The final experimental results show that when the enhanced model was compared with YOLOv7-tiny, the recognition accuracy was increased by 3%, and the recall rate was increased by 3.5%. These impressive results validated the superiority of the algorithm in terms of accuracy and localization ability within the target area. The algorithm effectively eliminates interferenced factors such as soil and background impurities, thereby enhancing its performance and making it an ideal choice for tasks such as cabbage transplantation state recognition. Conclusions The experimental results show that the proposed cabbage transplantation state detection method can meet the accuracy and real-time requirements for the identification of cabbage transplantation state, and the detection accuracy and localization accuracy of the improved model perform better when the target is smaller and there are weeds and other interferences in the background. Therefore, the method proposed in this study can improve the efficiency of cabbage transplantation quality measurement, reduce the time and labor, and improve the automation of field transplantation quality survey.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-06-17 Cooperative journals: 《智慧农业(中英文)》
Abstract: Objective With the development of agricultural informatization, a large amount of information about agricultural diseases exists in the form of text. However, due to problems such as nested entities and confusion of entity types, traditional named entities recognition (NER) methods often face challenges of low accuracy when processing agricultural disease text. To address this issue, this study proposes a new agricultural disease NER method called RoFormer-PointerNet, which combines the RoFormer pre-trained model with the PointerNet baseline model. The aim of this method is to improve the accuracy of entity recognition in agricultural disease text, providing more accurate data support for intelligent analysis, early warning, and prevention of agricultural diseases. Methods This method first utilized the RoFormer pre-trained model to perform deep vectorization processing on the input agricultural disease text. This step was a crucial foundation for the subsequent entity extraction task. As an advanced natural language processing model, the RoFormer pre-trained model’s unique rotational position embedding approach endowed it with powerful capabilities in capturing textual positional information. In agricultural disease text, due to the diversity of terminology and the existence of polysemy, traditional entity recognition methods often faced challenges in confusing entity types. However, through its unique positional embedding mechanism, the RoFormer model was able to incorporate more positional information into the vector representation, effectively enriching the feature information of words. This characteristic enabled the model to more accurately distinguish between different entity types in subsequent entity extraction tasks, reducing the possibility of type confusion. After completing the vectorization representation of the text, this study further emploied a pointer network for entity extraction. The pointer network was an advanced sequence labeling approach that utilizes head and tail pointers to annotate entities within sentences. This labeling method was more flexible compared to traditional sequence labeling methods as it was not restricted by fixed entity structures, enabling the accurate extraction of all types of entities within sentences, including complex entities with nested relationships. In agricultural disease text, entity extraction often faced the challenge of nesting, such as when multiple different entity types are nested within a single disease symptom description. By introducing the pointer network, this study effectively addressed this issue of entity nesting, improving the accuracy and completeness of entity extraction. Results and Discussions To validate the performance of the RoFormer-PointerNet method, this study constructed an agricultural disease dataset, which comprised 2 867 annotated corpora and a total of 10 282 entities, including eight entity types such as disease names, crop names, disease characteristics, pathogens, infected areas, disease factors, prevention and control methods, and disease stages. In comparative experiments with other pre-trained models such as Word2Vec, BERT, and RoBERTa, RoFormer-PointerNet demonstrated superiority in model precision, recall, and F1-Score, achieving 87.49%, 85.76% and 86.62%, respectively. This result demonstrated the effectiveness of the RoFormer pre-trained model. Additionally, to verify the advantage of RoFormer-PointerNet in mitigating the issue of nested entities, this study compared it with the widely used bidirectional long short-term memory neural network (BiLSTM) and conditional random field (CRF) models combined with the RoFormer pre-trained model as decoding methods. RoFormer-PointerNet outperformed the RoFormer-BiLSTM, RoFormer-CRF, and RoFormer-BiLSTM-CRF models by 4.8%, 5.67% and 3.87%, respectively. The experimental results indicated that RoFormer-PointerNet significantly outperforms other models in entity recognition performance, confirming the effectiveness of the pointer network model in addressing nested entity issues. To validate the superiority of the RoFormer-PointerNet method in agricultural disease NER, a comparative experiment was conducted with eight mainstream NER models such as BiLSTM-CRF, BERT-BiLSTM-CRF, and W2NER. The experimental results showed that the Ro‐ Former-PointerNet method achieved precision, recall, and F1-Score of 87.49%, 85.76% and 86.62%, respectively in the agricultural disease dataset, reaching the optimal level among similar methods. This result further verified the superior performance of the Ro‐ Former-PointerNet method in agricultural disease NER tasks. Conclusions The agricultural disease NER method RoFormer-PointerNet, proposed in this study and based on the RoFormer pretrained model, demonstrates significant advantages in addressing issues such as nested entities and type confusion during the entity extraction process. This method effectively identifies entities in Chinese agricultural disease texts, enhancing the accuracy of entity recognition and providing robust data support for intelligent analysis, early warning, and prevention of agricultural diseases. This research outcome holds significant importance for promoting the development of agricultural informatization and intelligence.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-06-17 Cooperative journals: 《智慧农业(中英文)》
Abstract: Significance Big Models, or Foundation Models, have offered a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate numerous parameters and have undergone extensive training, often showing excellent performance and adaptability, making them effective in addressing agricultural issues where data is limited. Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence, capable of processing diverse inputs, making informed decisions, and potentially overseeing entire farming systems autonomously. Progress The fundamental concepts and core technologies of big models are initially elaborated from five aspects: the generation and core principles of the Transformer architecture, scaling laws of extending big models, large-scale self-supervised learning, the general capabilities and adaptions of big models, and the emerging capabilities of big models. Subsequently, the possible application scenarios of the big model in the agricultural field are analyzed in detail, the development status of big models is described based on three types of the models: Large language models (LLMs), large vision models (LVMs), and large multi-modal models (LMMs). The progress of applying big models in agriculture is discussed, and the achievements are presented. Conclusions and Prospects The challenges and key tasks of applying big models technology in agriculture are analyzed. Firstly, the current datasets used for agricultural big models are somewhat limited, and the process of constructing these datasets can be both expensive and potentially problematic in terms of copyright issues. There is a call for creating more extensive, more openly accessible datasets to facilitate future advancements. Secondly, the complexity of big models, due to their extensive parameter counts, poses significant challenges in terms of training and deployment. However, there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency, thereby enhancing the performance of big models in agriculture. Thirdly, these advanced models demonstrate strong proficiency in analyzing image and text data, suggesting potential future applications in integrating real-time data from IoT devices and the Internet to make informed decisions, manage multi-modal data, and potentially operate machinery within autonomous agricultural systems. Finally, the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial. The public availability of these models is expected to refine their capabilities through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice, which could revolutionize agricultural practices.