Search

Text matching promotes the research and application of deep understanding of text information, and it provides the basis for information retrieval, recommendation systems and natural language processing by exploring the similar structures in text data. Owning to the outstanding performance and automatically extract text features for the target, the methods based-pre-training models gradually become the mainstream. However, such models usually suffer from the disadvantages of slow retrieval speed and low running efficiency. On the other hand, previous text matching algorithms have mainly focused on horizontal domain research, and there are relatively few vertical domain algorithms for agricultural text, which need to be further investigated. To address this issue, a second-order text matching algorithm has been developed. This paper first obtains a large amount of text about typical agricultural crops and constructs a database by using web crawlers and querying relevant textbooks, etc. Then BM25 algorithm is used to generate a candidate set and BERT model is used to filter the optimal match based on the candidate set. Experiments have shown that the Precision@1 of this second-order algorithm can reach 88.34% on the dataset constructed in this paper, and the average time to match a piece of text is only 2.02 s. Compared with BERT model and BM25 algorithm, there is an increase of 8.81% and 13.73% in Precision@1 respectively. In terms of the average time required for matching a text, it is 55.2 s faster than BERT model and only 2 s slower than BM25 algorithm. It can improve the efficiency and accuracy of agricultural information retrieval, agricultural decision support, agricultural market analysis, etc., and promote the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

The detection of tomatoes is of vital importance for enhancing production efficiency, with image recognition-based tomato detection methods being the primary approach. However, these methods face challenges such as the difficulty in extracting small targets, low detection accuracy, and slow processing speeds. Therefore, this paper proposes an improved RT-DETR-Tomato model for efficient tomato detection under complex environmental conditions. The model mainly consists of a Swin Transformer block, a BiFormer module, path merging, multi-scale convolutional layers, and fully connected layers. In this proposed model, Swin Transformer is chosen as the new backbone network to replace ResNet50 because of its superior ability to capture broader global dependency relationships and contextual information. Meanwhile, a lightweight BiFormer block is adopted in Swin Transformer to reduce computational complexity through content-aware flexible computation allocation. Experimental results show that the average accuracy of the final RT-DETR-Tomato model is greatly improved compared to the original model, and the model training time is greatly reduced, demonstrating better environmental adaptability. In the future, the RT-DETR-Tomato model can be integrated with intelligent patrol and picking robots, enabling precise identification of crops and ensuring the safety of crops and the smooth progress of agricultural production. [ABSTRACT FROM AUTHOR]

Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance. [ABSTRACT FROM AUTHOR]

The technology of rice potted-seedlings, which adopts a new type of transplanter, is one of the feasible approaches to explore the new models of rice planting mechanization in China. Plastic pots in the rice potted-seedling tray, which are independent of each other, are used to cultivate strong seedlings with a complete nutrition clod wrapped around their root systems. And then the seedlings are transplanted without injury in the field with a certain line of planting distance. In recent years, the research and extension have fully proved the production demonstration effect around China. In order to promote the combination and application of agricultural machinery with its supporting agronomy technology, this paper attempted to ascertain the rulers of tillers and the appropriate seedlings per hole for high-yield cultivation of rice potted-seedlings in mechanical transplanting. Wuyunjing24 was selected as the experimental material to investigate tillers emerging, panicles formation, and yield characteristics under different seedlings per hole. The results indicated: the tillers from the 1st to 9th tilling positions of the main stem and the simultaneous secondary tillers, which were greatly influenced by the seedlings per hole, had emerged and formed panicles in certain proportion. It was beneficial for the emerging of the low tillers from 1st and 2nd tilling positions when there were 1 or 2 strains of plants per plastic hole during the seedling period. Accordingly, the high tillers from the 8th tilling position and secondary tillers got a higher level of tilling rate during the field period. The number of final panicles was mainly from tillers with wide source parts in the maturation stage. The middle tillers from the 3rd and 4th tilling positions were promoted under the condition of 3 or 4 seedlings per hole, with the final panicles mainly from the 5th to 7th tilling positions while also a little bit low and the secondary tillers. When the seedlings per hole increased to 5 or 6 seedlings, the tilling rate and productive tiller percentage of each leaf significantly declined, with the final panicles mainly counting on the main stem and the middle tillers. The tillers from the middle positions showed a higher productive tiller percentage, and the tillers were the main source of the final panicles and the tiller panicles. The harvest yield of 5 or 6 seedlings per hole was lower than that from 3 or 4 seedlings per hole, but was higher than that from 1 or 2 seedlings per hole. There was a positive correlation between the population base and the effective panicles, and a negative correlation between seedlings per hole and grain per panicle; panicles and grain per panicle were the fundamental factors in deciding the yield. The suitable population base of the normal japonica rice, Wuyunjing24, with a large fringe, was 3-4 strains of plants per plastic hole, 3-4 seedlings per hole, through analysis of the multiple regression function of seedlings per hole on yield and consistent with the harvest yield. The experiment indicated the rules of tillers emergence and panicles formation for rice potted-seedling in mechanical transplanting, which provided a theoretical basis for the density of sowing and planting of the farm equipment in field production. According to the tilling characteristics of rice potted-seedlings, management measures of manure and water should be studied, for giving full play to the middle tillers of main stem. Appropriate seedlings per hole is the premise of high-yield cultivation, which particularly increases the grain yield, not only the panicle numbers, the grain numbers, setting percentage, and the gain weights but also for more harmonious yield factors. [ABSTRACT FROM AUTHOR]