Zhen-Zhen He, Xiao-Feng Chen, Yue Hou, Tie-Lin Yang, Bo Yang, Yan Guo
As a canonical non-B DNA secondary structure, the G-quadruplex (G4) dynamically regulates core biological processes, including telomere homeostasis, DNA replication and gene transcription/translation-through its unique four-stranded conformation. The significant enrichment of G4 structures in regulatory regions, particularly promoter regions within mammalian genomes reveals their critical role in transcriptional regulation. In this review, we focus on the dynamic formation mechanisms and transcriptional regulatory functions of endogenous G4 structures, systematically elucidating their three molecular pathways in modulating gene expression: (1) orchestrating spatial assembly of transcription activation complexes; (2) dynamically regulating epigenetic modifications, includinghistone alterations and DNA methylation; (3) remodeling three-dimensional chromatin architecture to establish transcriptionally active microenvironments. By integrating advancements in G4 topological characterization techniques and dynamic equilibrium networks, this work highlights the role of the G4 as a critical cis-regulatory element and provides a theoretical framework for developing G4-targeted therapeutic strategies.
{"title":"Advances in functional mechanisms of genomic G-quadruplex structures in transcriptional regulation.","authors":"Zhen-Zhen He, Xiao-Feng Chen, Yue Hou, Tie-Lin Yang, Bo Yang, Yan Guo","doi":"10.16288/j.yczz.25-055","DOIUrl":"https://doi.org/10.16288/j.yczz.25-055","url":null,"abstract":"<p><p>As a canonical non-B DNA secondary structure, the G-quadruplex (G4) dynamically regulates core biological processes, including telomere homeostasis, DNA replication and gene transcription/translation-through its unique four-stranded conformation. The significant enrichment of G4 structures in regulatory regions, particularly promoter regions within mammalian genomes reveals their critical role in transcriptional regulation. In this review, we focus on the dynamic formation mechanisms and transcriptional regulatory functions of endogenous G4 structures, systematically elucidating their three molecular pathways in modulating gene expression: (1) orchestrating spatial assembly of transcription activation complexes; (2) dynamically regulating epigenetic modifications, includinghistone alterations and DNA methylation; (3) remodeling three-dimensional chromatin architecture to establish transcriptionally active microenvironments. By integrating advancements in G4 topological characterization techniques and dynamic equilibrium networks, this work highlights the role of the G4 as a critical <i>cis</i>-regulatory element and provides a theoretical framework for developing G4-targeted therapeutic strategies.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"47 12","pages":"1287-1299"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mitochondria, as crucial organelles within eukaryotic cells, have their proteins and RNAs encoded by both the nuclear genome and the mitochondrial genome. They play vital roles in energy regulation, cellular metabolism, signal transduction, and various other physiological activities. Additionally, mitochondria interact with multiple organelles to collectively maintain cellular homeostasis. Mitochondria can also be transferred between cells and tissues through mechanisms such as migrasomes. Mitochondrial DNA (mtDNA) mutations often cause severe inherited rare diseases, characterized by tissue specificity, heterogeneity, multiple mutation sites, and challenges in achieving a complete cure. Gene editing of mtDNA holds promise for fundamentally curing such diseases. Traditional gene-editing nucleases, such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nuclease (TALENs), as well as novel gene editors like DddA-derived cytosine base editors (DdCBEs), have been demonstrated to correct certain mtDNA mutations. However, CRISPR-based technologies-despite their superior programmability and efficiency-are currently limited due to the technical bottleneck of inefficient sgRNA delivery into mitochondria. This article systematically reviews the structure and function of mitochondria, related diseases, and the current state of mtDNA gene-editing therapies. Furthermore, it explores future directions for optimizing therapeutic tools to overcome the challenge of sgRNA delivery, thereby addressing the treatment barriers posed by pathogenic mtDNA mutations in inherited rare diseases.
{"title":"Current understanding of mitochondrial DNA genetic diseases and gene therapy.","authors":"Cheng Tang, Shun-Qing Xu, Han-Zeng Li","doi":"10.16288/j.yczz.25-032","DOIUrl":"https://doi.org/10.16288/j.yczz.25-032","url":null,"abstract":"<p><p>Mitochondria, as crucial organelles within eukaryotic cells, have their proteins and RNAs encoded by both the nuclear genome and the mitochondrial genome. They play vital roles in energy regulation, cellular metabolism, signal transduction, and various other physiological activities. Additionally, mitochondria interact with multiple organelles to collectively maintain cellular homeostasis. Mitochondria can also be transferred between cells and tissues through mechanisms such as migrasomes. Mitochondrial DNA (mtDNA) mutations often cause severe inherited rare diseases, characterized by tissue specificity, heterogeneity, multiple mutation sites, and challenges in achieving a complete cure. Gene editing of mtDNA holds promise for fundamentally curing such diseases. Traditional gene-editing nucleases, such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nuclease (TALENs), as well as novel gene editors like DddA-derived cytosine base editors (DdCBEs), have been demonstrated to correct certain mtDNA mutations. However, CRISPR-based technologies-despite their superior programmability and efficiency-are currently limited due to the technical bottleneck of inefficient sgRNA delivery into mitochondria. This article systematically reviews the structure and function of mitochondria, related diseases, and the current state of mtDNA gene-editing therapies. Furthermore, it explores future directions for optimizing therapeutic tools to overcome the challenge of sgRNA delivery, thereby addressing the treatment barriers posed by pathogenic mtDNA mutations in inherited rare diseases.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"47 12","pages":"1300-1325"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the inherent complexity, hierarchical organization, and dynamic nature of living systems, there is no single best strategy for investigation, and priorities shift with the evolution of the life sciences. In the 1990s, two classic stories, The Salvation of Doug and The Demise of Bill, used automobiles as analogies and satire to contrast two research strategies: dismantling components to uncover underlying mechanisms, or applying functional perturbations to identify critical elements. These heuristic parables stimulated broad discussion on the respective strengths and limitations of different research approaches and continue to be widely used in teaching today. The life sciences have since entered an era integrating high-throughput, high-resolution, and multidimensional approaches, where single-path strategies can no longer provide deep, systematic insights into complex biological processes. We view the intrinsic features of living systems, such as modular organization, regulatory networks, nonlinear responses, and adaptive compensation, as factors that make any single approach likely to capture only local, static aspects, thereby hindering the reconstruction of systems-level, dynamic properties. Against this backdrop, we present a modern continuation of the two parables, reimagined in a contemporary setting and featuring two protagonists with symbolic Chinese names, "Zhiwei" (meaning "decoding hidden mechanisms") and "Sixu" ("reasoning through order"), who personify biochemical and genetic mindsets. In our narrative, the two protagonists transition from working independently to collaborating, integrating high-throughput experimentation, systems-level analysis, and computational modeling to uncover structural and operational principles underlying complex systems. We believe this retelling reflects the growing emphasis on systems-level and dynamic perspectives in biology, highlighting the value of methodological integration and innovation. We hope it will serve as a valuable resource for teaching in genetics and related disciplines, while fostering reflection on the enduring relevance of genetic reasoning in contemporary research.
{"title":"A journey into biological complexity: continuing the legacy of Doug and Bill.","authors":"Miao-Ling Yang, Zhuo Du","doi":"10.16288/j.yczz.25-182","DOIUrl":"https://doi.org/10.16288/j.yczz.25-182","url":null,"abstract":"<p><p>Given the inherent complexity, hierarchical organization, and dynamic nature of living systems, there is no single best strategy for investigation, and priorities shift with the evolution of the life sciences. In the 1990s, two classic stories, <i>The Salvation of Doug</i> and <i>The Demise of Bill</i>, used automobiles as analogies and satire to contrast two research strategies: dismantling components to uncover underlying mechanisms, or applying functional perturbations to identify critical elements. These heuristic parables stimulated broad discussion on the respective strengths and limitations of different research approaches and continue to be widely used in teaching today. The life sciences have since entered an era integrating high-throughput, high-resolution, and multidimensional approaches, where single-path strategies can no longer provide deep, systematic insights into complex biological processes. We view the intrinsic features of living systems, such as modular organization, regulatory networks, nonlinear responses, and adaptive compensation, as factors that make any single approach likely to capture only local, static aspects, thereby hindering the reconstruction of systems-level, dynamic properties. Against this backdrop, we present a modern continuation of the two parables, reimagined in a contemporary setting and featuring two protagonists with symbolic Chinese names, \"Zhiwei\" (meaning \"decoding hidden mechanisms\") and \"Sixu\" (\"reasoning through order\"), who personify biochemical and genetic mindsets. In our narrative, the two protagonists transition from working independently to collaborating, integrating high-throughput experimentation, systems-level analysis, and computational modeling to uncover structural and operational principles underlying complex systems. We believe this retelling reflects the growing emphasis on systems-level and dynamic perspectives in biology, highlighting the value of methodological integration and innovation. We hope it will serve as a valuable resource for teaching in genetics and related disciplines, while fostering reflection on the enduring relevance of genetic reasoning in contemporary research.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"47 12","pages":"1377-1386"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.04.001
Antonino GALATI, Serena SOFIA, Maria CRESCIMANNO
Precision farming technologies are revolutionising the wine-growing sector thanks to their ability to manage crop variability, increase economic benefits, reduce the environmental impact, and improve grape yields and quality. Most earlier studies focused on the effects of precision technology adoption on plant health and canopy development—and therefore grape quality—neglecting the profitability impact. This study aims to fill this gap by presenting a systematic literature analysis discussing advancements in the economics of precision viticulture technologies. The results show how technologies such as unmanned aerial vehicles, precision irrigation, and robotics can increase efficiency in resource management, helping to reduce costs and improve vineyard profitability. However, the findings also emphasise the need for tailored approaches to integrate these advances. Furthermore, the analysis highlights the main barriers related to the cost of adopting precision technologies and the skills required to read and interpret the data. The results of this study hold interest to academics, vine growers, and farmers, providing a basis for future research into the cost-effectiveness of adopting precision technologies.
{"title":"Economics and barriers of precision viticulture technologies: A comprehensive systematic literature review","authors":"Antonino GALATI, Serena SOFIA, Maria CRESCIMANNO","doi":"10.1016/j.inpa.2025.04.001","DOIUrl":"10.1016/j.inpa.2025.04.001","url":null,"abstract":"<div><div>Precision farming technologies are revolutionising the wine-growing sector thanks to their ability to manage crop variability, increase economic benefits, reduce the environmental impact, and improve grape yields and quality. Most earlier studies focused on the effects of precision technology adoption on plant health and canopy development—and therefore grape quality—neglecting the profitability impact. This study aims to fill this gap by presenting a systematic literature analysis discussing advancements in the economics of precision viticulture technologies. The results show how technologies such as unmanned aerial vehicles, precision irrigation, and robotics can increase efficiency in resource management, helping to reduce costs and improve vineyard profitability. However, the findings also emphasise the need for tailored approaches to integrate these advances. Furthermore, the analysis highlights the main barriers related to the cost of adopting precision technologies and the skills required to read and interpret the data. The results of this study hold interest to academics, vine growers, and farmers, providing a basis for future research into the cost-effectiveness of adopting precision technologies.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 487-500"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.07.001
Xinhao Zhang , Guangpeng Zhang , Jiayi Wang , Jinqi Yang , Quanqu Ge , Ran Zhao , Yang Wang
The labor cost in agriculture is gradually increasing, making it necessary to develop robots for strawberry picking. These robots require accurate strawberry localization, which remains challenging using machine vision. While instance segmentation can improve positioning accuracy, current algorithms are inefficient on edge computing devices during robot navigation and ineffective for recognizing strawberries in elevated cultivation. This paper proposes an improved YOLOv8n model (YOLOv8n-MCP) optimized for edge computing during robot navigation. The network implements three key improvements: 1) MobileNetV3 as the backbone, enhancing strawberry feature extraction under varied lighting while reducing parameters and GFLOPs; 2) a new Cross-scale Feature Fusion Module (CCFM) as the Neck, improving detection of strawberries at varying distances; and 3) Partial Convolution (PConv) to enhance C2f and Head components, further reducing network parameters and GFLOPs while improving FPS. Experimental results show that compared to YOLOv8n, YOLOv8n-MCP reduces parameters by 69 %, GFLOPs by 56 %, and increases FPS by 42 %. Tests on Nvidia Jetson Xavier NX demonstrate that YOLOv8n-MCP achieves 49.5 FPS, significantly outperforming the original YOLOv8n’s 37.6 FPS, effectively meeting the requirements for strawberry instance segmentation during robot navigation with edge devices.
{"title":"Efficient instance segmentation for strawberry in greenhouses using YOLOv8n-MCP on edge devices","authors":"Xinhao Zhang , Guangpeng Zhang , Jiayi Wang , Jinqi Yang , Quanqu Ge , Ran Zhao , Yang Wang","doi":"10.1016/j.inpa.2025.07.001","DOIUrl":"10.1016/j.inpa.2025.07.001","url":null,"abstract":"<div><div>The labor cost in agriculture is gradually increasing, making it necessary to develop robots for strawberry picking. These robots require accurate strawberry localization, which remains challenging using machine vision. While instance segmentation can improve positioning accuracy, current algorithms are inefficient on edge computing devices during robot navigation and ineffective for recognizing strawberries in elevated cultivation. This paper proposes an improved YOLOv8n model (YOLOv8n-MCP) optimized for edge computing during robot navigation. The network implements three key improvements: 1) MobileNetV3 as the backbone, enhancing strawberry feature extraction under varied lighting while reducing parameters and GFLOPs; 2) a new Cross-scale Feature Fusion Module (CCFM) as the Neck, improving detection of strawberries at varying distances; and 3) Partial Convolution (PConv) to enhance C2f and Head components, further reducing network parameters and GFLOPs while improving FPS. Experimental results show that compared to YOLOv8n, YOLOv8n-MCP reduces parameters by 69 %, GFLOPs by 56 %, and increases FPS by 42 %. Tests on Nvidia Jetson Xavier NX demonstrate that YOLOv8n-MCP achieves 49.5 FPS, significantly outperforming the original YOLOv8n’s 37.6 FPS, effectively meeting the requirements for strawberry instance segmentation during robot navigation with edge devices.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 539-549"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.02.006
Xincai Yu , Shuangyin Liu , Chenjiaozi Wang , Binbin Jiao , Cong Huang , Bo Liu , Conghui Liu , Liping Yin , Fanghao Wan , Wanqiang Qian , Xi Qiao
Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi (Phytophthora citrophthora, Phytophthora citricola, Phytophthora syringae). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.
{"title":"Detection of fungal disease in citrus fruit based on hyperspectral imaging","authors":"Xincai Yu , Shuangyin Liu , Chenjiaozi Wang , Binbin Jiao , Cong Huang , Bo Liu , Conghui Liu , Liping Yin , Fanghao Wan , Wanqiang Qian , Xi Qiao","doi":"10.1016/j.inpa.2025.02.006","DOIUrl":"10.1016/j.inpa.2025.02.006","url":null,"abstract":"<div><div>Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi (<em>Phytophthora citrophthora</em>, <em>Phytophthora citricola</em>, <em>Phytophthora syringae</em>). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 456-465"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The medicinal preparation of Chilobrachys jingzhao possesses various therapeutic properties, including anti-inflammatory, detoxifying, analgesic, and anti-edema effects. However, research on its genetic background and toxin mechanisms is held back by the lack of chromosome karyotype and genome data. In this study, we analyzed the karyotype of C. jingzhao using chromosome preparation techniques, estimated the genome size using flow cytometry and K-mer analysis, and performed genome sequencing and assembly using second- and third-generation single molecule real-time sequencing technologies. The results showed that C. jingzhao has a diploid chromosome number of 2n=68, with a karyotype formula of 2n=46m+18sm+4st and a chromosomal complement of 2n=10L+18M2+38M1+2S. Using Solanum lycopersicum and Trichonephila clavata as references, flow cytometry estimates the genome size at 7,775.49 Mb and 7,680.26 Mb, respectively. The 19-mer analysis also estimated the genome size to be 7,626.00 Mb, consistent with the flow cytometry results. Further analysis indicated that the genome of C. jingzhao has a high level of heterozygosity (8.45%) and a high proportion of repetitive sequences (67.10%), classifying it as an ultra-high heterozygous and high-repeat genome. The initial genome assembly of C. jingzhao was 8,804.93 Mb in size, with a contig N50 of 55.55 Mb and a BUSCO completeness score of 95.9%, indicating high assembly quality. This study first reveals the karyotype and genome information of C. jingzhao, offering crucial data for future research on its whole genome, toxin mechanisms, genetics, origin, evolution, and taxonomy.
{"title":"Karyotype and genome characterization analysis of <i>Chilobrachys jingzhao</i> (Theraphosidae: <i>Chilobrachys</i>).","authors":"Yu-Xuan Zhang, Meng-Ying Zhang, Han-Ting Yang, Chi Song, Zi-Zhong Yang, Shi-Lin Chen","doi":"10.16288/j.yczz.25-026","DOIUrl":"https://doi.org/10.16288/j.yczz.25-026","url":null,"abstract":"<p><p>The medicinal preparation of <i>Chilobrachys jingzhao</i> possesses various therapeutic properties, including anti-inflammatory, detoxifying, analgesic, and anti-edema effects. However, research on its genetic background and toxin mechanisms is held back by the lack of chromosome karyotype and genome data. In this study, we analyzed the karyotype of <i>C. jingzhao</i> using chromosome preparation techniques, estimated the genome size using flow cytometry and K-mer analysis, and performed genome sequencing and assembly using second- and third-generation single molecule real-time sequencing technologies. The results showed that <i>C. jingzhao</i> has a diploid chromosome number of 2<i>n</i>=68, with a karyotype formula of 2<i>n</i>=46m+18sm+4st and a chromosomal complement of 2<i>n</i>=10L+18M2+38M1+2S. Using <i>Solanum lycopersicum</i> and <i>Trichonephila clavata</i> as references, flow cytometry estimates the genome size at 7,775.49 Mb and 7,680.26 Mb, respectively. The 19-mer analysis also estimated the genome size to be 7,626.00 Mb, consistent with the flow cytometry results. Further analysis indicated that the genome of <i>C. jingzhao</i> has a high level of heterozygosity (8.45%) and a high proportion of repetitive sequences (67.10%), classifying it as an ultra-high heterozygous and high-repeat genome. The initial genome assembly of <i>C. jingzhao</i> was 8,804.93 Mb in size, with a contig N50 of 55.55 Mb and a BUSCO completeness score of 95.9%, indicating high assembly quality. This study first reveals the karyotype and genome information of <i>C. jingzhao</i>, offering crucial data for future research on its whole genome, toxin mechanisms, genetics, origin, evolution, and taxonomy.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"47 12","pages":"1351-1364"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated vision-based detection and counting are critical for accurate tomato yield estimation, which contribute to precise yield management strategies and an efficient food supply chains. Special conditions, including background clutter, occlusion, and varying sunlight, affect the accuracy of crop detection and counting. To determine the most suitable algorithms for this yield estimation context, we herein establish a public multi-object tracking (MOT) dataset for tomato cluster counts, while evaluating and comparing state-of-the-art target detection and MOT-based algorithms. The evaluated detectors consist of YOLOv8 and RT-DETR, which represent algorithms that achieve a balance between accuracy and speed. The tracking algorithms included state-of-the-art methodologies such as SORT, DeepSort, ByteTrack, and BotSort. Initially, the performance of the detectors was rigorously evaluated, followed by a comprehensive assessment of the four tracking algorithms within a multi-target tracking database tailored for this research and structured in the MOT context. The findings reveal that YOLOv8 and RT-DETR achieve 93.6% and 94.9% results at mAP@75, respectively, with RT-DETR exhibiting fewer false detections. When combined with the RT-DETR detector, the ByteTrack-based algorithm registers the highest counting accuracy at 95.5%, whereas BotSort achieves the highest MOTA score with 84.6%. Notably, the trackers without the ReID module (i.e., SORT and ByteTrack) demonstrate greater adaptability to frame rate variations in the test videos. At a 30-fps frame rate, the incorporation of ReID modules in DeepSort and BotSort algorithms significantly enhances the MOTA metric. Looking ahead, we plan to leverage these algorithms into an autonomous inspection platform that aims to estimate crop yield in real-time.
{"title":"Assessment of the tomato cluster yield estimation algorithms via tracking-by-detection approaches","authors":"Zhongxian Qi , Tianxue Zhang , Ting Yuan , Wei Zhou , Wenqiang Zhang","doi":"10.1016/j.inpa.2025.02.005","DOIUrl":"10.1016/j.inpa.2025.02.005","url":null,"abstract":"<div><div>Automated vision-based detection and counting are critical for accurate tomato yield estimation, which contribute to precise yield management strategies and an efficient food supply chains. Special conditions, including background clutter, occlusion, and varying sunlight, affect the accuracy of crop detection and counting. To determine the most suitable algorithms for this yield estimation context, we herein establish a public multi-object tracking (MOT) dataset for tomato cluster counts, while evaluating and comparing state-of-the-art target detection and MOT-based algorithms. The evaluated detectors consist of YOLOv8 and RT-DETR, which represent algorithms that achieve a balance between accuracy and speed. The tracking algorithms included state-of-the-art methodologies such as SORT, DeepSort, ByteTrack, and BotSort. Initially, the performance of the detectors was rigorously evaluated, followed by a comprehensive assessment of the four tracking algorithms within a multi-target tracking database tailored for this research and structured in the MOT context. The findings reveal that YOLOv8 and RT-DETR achieve 93.6% and 94.9% results at mAP@75, respectively, with RT-DETR exhibiting fewer false detections. When combined with the RT-DETR detector, the ByteTrack-based algorithm registers the highest counting accuracy at 95.5%, whereas BotSort achieves the highest MOTA score with 84.6%. Notably, the trackers without the ReID module (i.e., SORT and ByteTrack) demonstrate greater adaptability to frame rate variations in the test videos. At a 30-fps frame rate, the incorporation of ReID modules in DeepSort and BotSort algorithms significantly enhances the MOTA metric. Looking ahead, we plan to leverage these algorithms into an autonomous inspection platform that aims to estimate crop yield in real-time.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 445-455"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.07.003
Helong Yu , Liyun Han , Chengcheng Chen , Honghong Su , Qichao Niu , Ronghao Meng , Mingxuan Xue
The accurate counting of overlapping watermelon seeds is a key foundation for seed quality testing, breeding selection, resource allocation, and other processes. To improve the counting accuracy for flat and slightly overlapping seeds, we introduce LOYOLO-GC, a Lightweight Occlusion YOLO8n-based group counting model. It adopts HGNetV2 as its backbone, where HGBlocks extract multi-level features for improved learning. GhostConv replaces the standard convolution in HGBlocks, forming LightHGBlock to reduce the number of parameters by generating intrinsic and ghost feature maps with fewer kernels. In addition, a Large Separable Kernel Attention mechanism (LSKA) is used to decompose deep convolution kernels into horizontal and vertical 1D kernels, enabling efficient large kernel attention with lower computational and memory cost. After optimizing the model, we build a multi-occlusion watermelon seed dataset and employ it to develop a LOYOLO-based group counting method. The experimental results show that LOYOLO-GC outperforms SOTA models, achieving 96.08 % accuracy and 86.66 % mAP, an improvement of 0.48 % and 1.67 %, respectively. The model parameters decrease by 63.8 % and GMACs decrease by 38.9 %. Counting accuracy is also improved, with ACC increasing by 5.32 % and L-ACC increasing by 5.04 %, while MAE and RMSE are decreased by 3.68 and 3.28, respectively.
{"title":"Lightweight precision model for watermelon seed group density estimation and counting","authors":"Helong Yu , Liyun Han , Chengcheng Chen , Honghong Su , Qichao Niu , Ronghao Meng , Mingxuan Xue","doi":"10.1016/j.inpa.2025.07.003","DOIUrl":"10.1016/j.inpa.2025.07.003","url":null,"abstract":"<div><div>The accurate counting of overlapping watermelon seeds is a key foundation for seed quality testing, breeding selection, resource allocation, and other processes. To improve the counting accuracy for flat and slightly overlapping seeds, we introduce LOYOLO-GC, a Lightweight Occlusion YOLO8n-based group counting model. It adopts HGNetV2 as its backbone, where HGBlocks extract multi-level features for improved learning. GhostConv replaces the standard convolution in HGBlocks, forming LightHGBlock to reduce the number of parameters by generating intrinsic and ghost feature maps with fewer kernels. In addition, a Large Separable Kernel Attention mechanism (LSKA) is used to decompose deep convolution kernels into horizontal and vertical 1D kernels, enabling efficient large kernel attention with lower computational and memory cost. After optimizing the model, we build a multi-occlusion watermelon seed dataset and employ it to develop a LOYOLO-based group counting method. The experimental results show that LOYOLO-GC outperforms SOTA models, achieving 96.08 % accuracy and 86.66 % mAP, an improvement of 0.48 % and 1.67 %, respectively. The model parameters decrease by 63.8 % and GMACs decrease by 38.9 %. Counting accuracy is also improved, with ACC increasing by 5.32 % and L-ACC increasing by 5.04 %, while MAE and RMSE are decreased by 3.68 and 3.28, respectively.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 565-580"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate recognition of apple leaf diseases is crucial for ensuring crop health and agricultural productivity. However, deep learning models often suffer from poor generalization across diverse environments due to variations in lighting, background complexity, and leaf appearance. To address these challenges, we proposed EConv-ViT, a novel robust generalization model integrating ConvNeXt and Vision Transformer (ViT), enhanced with Efficient Channel Attention (ECA) for superior feature extraction and DropKey to improve generalization and applied the mode on image dataset both captured in laboratory and natural environments for healthy apple leaves, alternaria blotch, grey spot, rust, and mosaic disease. The propsed EConv-ViT model was tested on an independent dataset and achieved accuracy of 99.2% on laboratory-captured image dataset and 79.3% on images captured in natural environments. The classification accuracy for EConv-ViT model exhibited 18.6%, 36.1% and 37.8% improvements compared with ViT, ConvNeXt, and ResNet50 models on a dataset captured in natural environments. EConv-ViT can effectively capture both local and global features and demonstrate its potential for the application on related automated disease monitoring systems.
{"title":"EConv-ViT: A strongly generalized apple leaf disease classification model based on the fusion of ConvNeXt and Transformer","authors":"Xin Huang , Demin Xu , Yongqiao Chen , Qian Zhang , Puyu Feng , Yuntao Ma , Qiaoxue Dong , Feng Yu","doi":"10.1016/j.inpa.2025.03.001","DOIUrl":"10.1016/j.inpa.2025.03.001","url":null,"abstract":"<div><div>The accurate recognition of apple leaf diseases is crucial for ensuring crop health and agricultural productivity. However, deep learning models often suffer from poor generalization across diverse environments due to variations in lighting, background complexity, and leaf appearance. To address these challenges, we proposed EConv-ViT, a novel robust generalization model integrating ConvNeXt and Vision Transformer (ViT), enhanced with Efficient Channel Attention (ECA) for superior feature extraction and DropKey to improve generalization and applied the mode on image dataset both captured in laboratory and natural environments for healthy apple leaves, alternaria blotch, grey spot, rust, and mosaic disease. The propsed EConv-ViT model was tested on an independent dataset and achieved accuracy of 99.2% on laboratory-captured image dataset and 79.3% on images captured in natural environments. The classification accuracy for EConv-ViT model exhibited 18.6%, 36.1% and 37.8% improvements compared with ViT, ConvNeXt, and ResNet50 models on a dataset captured in natural environments. EConv-ViT can effectively capture both local and global features and demonstrate its potential for the application on related automated disease monitoring systems.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 466-477"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}