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ObjectiveWith 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.MethodsThis 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 DiscussionsTo 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 RoFormer-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 RoFormer-PointerNet method in agricultural disease NER tasks.ConclusionsThe agricultural disease NER method RoFormer-PointerNet, proposed in this study and based on the RoFormer pre-trained 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.

SignificanceBig 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.ProgressThe 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 ProspectsThe 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.

ObjectiveKnowledge services in agricultural scenarios have the characteristics of long periodicity and prolonged activity time. Traditional recommendation models cannot effectively mine hidden information in agricultural scenarios, in order to improve the quality of agricultural knowledge recommendation services, agricultural contextual information based on agricultural time should be fully considered. To address these issues, a Time-aware and filter-enhanced sequential recommendation model for agricultural knowledge (TiFSA) was proposed, integrating temporal perception and enhanced filtering.MethodsFirst, based on the temporal positional embedding, combining the temporal information of farmers' interactions with positional embedding based on time perception, it helped to learn project relevance based on agricultural season in agricultural contexts. A multi-head self-attention network recommendation algorithm based on time-awareness was proposed for the agricultural knowledge recommendation task, which extracted different interaction time information in the user interaction sequence and introduced it into the multi-head self-attention network to calculate the attention weight, which encoded the user's periodic interaction information based on the agricultural time, and also effectively captured the user's dynamic preference information over time. Then, through the temporal positional embedding, a filter filtering algorithm was introduced to adaptively attenuate the noise in farmers' situational data adaptively. The filtering algorithm was introduced to enhance the filtering module to effectively filter the noisy information in the agricultural dataset and alleviate the overfitting problem due to the poorly normalized and sparse agricultural dataset. By endowing the model with lower time complexity and adaptive noise attenuation capability. The applicability of this method in agricultural scenarios was improved. Next, a multi-head self attention network with temporal information was constructed to achieve unified modeling of time, projects, and features, and represent farmers' preferences of farmers over time in context, thereby providing reliable recommendation results for users. Finally, the AdamW optimizer was used to update and compute the model parameters. AdamW added L2 regularization and an appropriate penalty mechanism for larger weights, which could update all weights more smoothly and alleviate the problem of falling into local minima. Applied in the field of agricultural recommendation, it could further improve the training effect of the model. The experimental data came from user likes, comments, and corresponding time information in the "National Agricultural Knowledge Intelligent Service Cloud Platform", and the dataset ml-1m in the movie recommendation scenario was selected as an auxiliary validation of the performance of this model.Results and DiscussionsAccording to the user interaction sequence datasets in the "National Agricultural Knowledge Intelligent Service Cloud Platform", from the experimental results, it could be learned that TiFSA outperforms the other models on two different datasets, in which the enhancement was more obvious on the Agriculture dataset, where HR and NDCG were improved by 14.02% and 16.19%, respectively, compared to the suboptimal model, TiSASRec; while on the ml-1m dataset compared to the suboptimal model, SASRec, HR and NDCG were improved by 1.90% and 2.30%, respectively. In summary, the TiFSA model proposed in this paper has a large improvement compared with other models, which verifies verified the effectiveness of the TiFSA model and showed that the time interval information of farmer interaction and the filtering algorithm play an important role in the improvement of the model performance in the agricultural context. From the results of the ablation experiments, it could be seen that when the time-aware and enhanced filtering modules were removed, the values of the two metrics HR@10 and NDCG@10 were 0.293 6 and 0.203 9, respectively, and the recommended performance was poor. When only the time-aware module and only the augmentation filtering module were removed, the experimental results had different degrees of improvement compared to TiFSA-tf, and the TiFSA model proposed in this paper achieved the optimal performance in the two evaluation metrics. When only the multi-head self-attention network was utilized for recommendation, both recommendation metrics of the model were lower, indicating that the traditional sequence recommendation method that only considered the item number was not applicable to agricultural scenarios. When the augmented filtering module was introduced without the time-aware module, the model performance was improved, but still failed to achieve the ideal recommendation effect. When only the time-aware module was introduced without the augmented filtering module, there was a significant improvement in the model effect, which proved that the time-aware module was more applicable to agricultural scenarios and can effectively improve the model performance of the sequence recommendation task. When both time-aware and augmented filtering modules were introduced, the model performance was further improved, which on the one hand illustrated the dependence of the augmented filtering module on the time-aware module, and on the other hand verified the necessity of adopting the augmented filtering to the time-aware self-attention network model.ConclusionsThis research proposes an agricultural knowledge recommendation model that integrates time-awareness and augmented filtering, which introduces the user's interaction time interval into the embedded information, so that the model effectively learns the information of agricultural time in the agricultural scene, and the prediction of the user's interaction time and the object is more closely related to the actual scene; augmented filtering algorithms are used to attenuate the noise in the agricultural data. At the same time, the enhanced filtering algorithm is used to attenuate the noise in the agricultural data, and can be effectively integrated into the model for use, further improving the recommendation performance of the model. The experimental results show the effectiveness of the proposed TiFSA model on the agricultural dataset. The ablation experiments confirm the positive effect of time-awareness and enhanced filtering modules on the improvement of recommendation performance.

Transplanting is a critical step in vegetable production, and the application of automatic transplanters can significantly reduce labor intensity, improve production efficiency, and enhance the precision and consistency of operations. However, automatic transplanters are structurally complex, with diverse components, each design and function offering its own advantages and limitations. To assist industry professionals in quickly understanding and selecting transplanters suited to specific crops and environments, this paper reviews three key technologies in current vegetable transplanters: planting mechanisms, automated seedling picking and placing, and tray conveyance. Each technology is classified, compared, and analyzed to evaluate its applicability. Based on the current state of technology, the paper identifies major challenges in the development of vegetable transplanters in China, including insufficient integration of machinery and agronomy, high demands for equipment adaptability, lack of standardized systems, and delays in the development of core technologies for fully automated transplanting. Solutions are proposed for each of these issues. Finally, the paper discusses future directions for the development of automatic transplanters, including enhancing transplanting efficiency, achieving autonomous navigation, digitalizing operations, developing supporting systems for transplanting, and unmanned transplanting. [ABSTRACT FROM AUTHOR]

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation. [ABSTRACT FROM AUTHOR]

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms. [ABSTRACT FROM AUTHOR]

To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm's focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys' automation levels. [ABSTRACT FROM AUTHOR]