Plant detection and counting play a crucial role in modern agriculture, providing vital references for precision management resource allocation. This study follows the footsteps of machine learning experts by introducing state-of-the-art Yolov8 technology into field plant science. Moreover, we made some simple yet effective improvements. The integration shallow-level information path aggregation network (PANet) served to counterbalance resolution loss stemming from expanded receptive field....

Introduction Soybean pod count is one of the crucial indicators soybean yield. Nevertheless, due to challenges associated with counting pods, such as crowded and uneven distribution, existing models prioritize accuracy over efficiency, which does not meet requirements for lightweight real-time tasks. Methods To address this goal, we have designed a deep convolutional network called PodNet. It employs encoder an efficient decoder that effectively decodes both shallow information, alleviating...

Detection and counting of wheat heads are crucial importance in the field plant science, as they can be used for crop management, yield prediction, phenotype analysis. With widespread application computer vision technology monitoring automated high-throughput phenotyping platforms has become possible. Currently, many innovative methods new technologies have been proposed that made significant progress accuracy robustness head recognition. Nevertheless, these often built on high-performance...

As modern agricultural technology advances, the automated detection, classification, and harvesting of strawberries have become an inevitable trend. Among these tasks, classification stands as a pivotal juncture. Nevertheless, existing object detection methods struggle with substantial computational demands, high resource utilization, reduced efficiency. These challenges make deployment on edge devices difficult lead to suboptimal user experiences. In this study, we developed lightweight...

Monitoring rice spikelet yield is crucial for ensuring food security, but manual observations are tedious and subjective. Deep learning approaches automated counting often require high device resources, limiting their applicability on low-cost edge devices. This paper presents the Rice Lightweight Feature Detection Network (RLFDNet). RLFDNet designed field of computer vision, features a lightweight encoder decoder, effectively decoding shallow deep information within its neural network...

Text Sentiment Analysis (TSA) is becoming a hot area of research in the field Natural Language Processing (NLP). There are many researches on English text sentiment analysis, while Chinese do not attract sufficient attention. In this paper, we provide novel deep learning model called Bilinear Character-Word Convolutional Neural Networks (BCWCNN) to deal with analysis task. Our represent sentence as bilinear combination features learned from two-stream CNN models, which receives...

In recent years, deep learning has garnered widespread attention in graph-structured data. Nevertheless, due to the high cost of collecting labeled graph data, domain adaptation becomes particularly crucial supervised tasks. The performance existing methods may degrade when there are disparities between training and testing especially challenging scenarios such as remote sensing image analysis. this study, an approach achieving high-quality without explicit was explored. proposed Efficient...

This article aims to analyze the experience of learning practical English and explore its importance in contemporary society.Firstly, definition application areas are introduced, value is emphasized.The text discusses challenges English, such as language barriers, cultural differences, limitations resources.Next, some effective strategies proposed, enhancing listening skills, improving oral expression, strengthening reading writing creating opportunities.In addition, case studies learning,...

With the development of globalization and increase in international communication, application practical English work vocational students has become increasingly important.The purpose this paper is to explore significance for suggest training pathways analyze cases.First, article introduces needs at challenges cross-cultural communication.The discussion then focused on classroom teaching methods opportunities developing skills among higher students.By analyzing cases interviews communication...

With the development of deep learning technology, object detection has been widely applied in various fields. However, cross-dataset detection, conventional models often face performance degradation issues. This is particularly true agricultural field, where there a multitude crop types and complex variable environment. Existing technologies still bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes lightweight, enhanced method for domain based on...

Amino acid network is the reduced representation of protein structure, which benefits investigation structure. And paradigm offers an insight into viewing global connectivity and studying interaction between secondary structures by visualizing amino networks. However, there exists some disadvantages in current research. Much research about are based on contact map, but it cannot provide easy way to present local regularity topology structures. Until recently consider primary structure cause...

Abstract Background: Detection and counting of wheat heads are crucial importance in the field plant science, as they can be used for crop management, yield prediction, phenotype analysis. With widespread application computer vision technology monitoring automated high-throughput phenotyping platforms has become possible. Currently, many innovative methods new technologies have been proposed that made significant progress accuracy robustness head recognition. Nevertheless, these often built...