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RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

作物学报:英文版 2023年第5期11卷 1586-1594页

作者： Xiaodong Bai susong gu Pichao Liu Aiping Yang Zhe Cai Jianjun Wang Jianguo Yao School of Computer Science and Technology Hainan UniversityHaikou 570228HainanChina Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210003JiangsuChina Agricultural Meteorological Center Jiangxi Meteorological BureauNanchang 330045JiangxiChina

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

关键词： Rice Precision agriculture Plant counting Deep learning Attention mechanism

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RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

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The Crop Journal 2023年第5期

作者： Xiaodong Bai susong gu Pichao Liu Aiping Yang Zhe Cai Jianjun Wang Jianguo Yao

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From Green Super Rice to green agriculture:Reaping the promise of functional genomics research

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Molecular Plant 2022年第1期15卷 9-26页

作者： Sibin Yu Jauhar Ali Shaochuan Zhou guangjun Ren Huaan Xie Jianlong Xu Xinqiao Yu Fasong Zhou Shaobing Peng Liangyong Ma Dingyang Yuan Zefu Li Dazhou Chen Ruifeng Zheng Zhigang Zhao Chengcai Chu Aiqing You Yu Wei susong Zhu Qiongyao gu guangcun He Shigui Li guifu Liu Changhua Liu Chaopu Zhang Jinghua Xiao Lijun Luo Zhikang Li Qifa Zhang National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China International Rice Research Institute DAPO Box 7777Metro ManilaPhilippines guangdong Academy of Agricultural Sciences GuangzhouChina Sichuan Academy of Agricultural Sciences ChengduChina Fujian Academy of Agricultural Sciences FuzhouChina Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijingChina Shanghai Agrobiological Gene Center ShanghaiChina China National Seed Group Co. LtdBeijingChina China National Rice Research Institute HangzhouChina Hunan National Hybrid Rice Center ChangshaChina Anhui Academy of Agricultural Sciences HefeiChina Jiangxi Academy of Agricultural Sciences NanchangChina Nanjing Agricultural University NanjingChina State Key Laboratory of Plant Genomics Institute of Genetics and Developmental BiologyThe Innovative Academy for Seed DesignChinese Academy of SciencesBeijingChina Hubei Academy of Agricultural Sciences WuhanChina guangxi Academy of Agricultural Sciences NanningChina guizhou Academy of Agricultural Sciences GuiyangChina Yunnan Academy of Agricultural Sciences KunmingChina Wuhan University WuhanChina Sichuan Agricultural University ChengduChina Heilongjiang Academy of Agricultural Sciences HarbinChina College of Agronomy Anhui Agricultural UniversityHefeiChina Wuhan Greenfafa Institute of Novel Genechip R&D China

Producing sufficient food with finite resources to feed the growing global population while having a smaller impact on the environment has always been a great ***,we review the concept and practices of Green Super Rice(GSR)that have led to a paradigm shift in goals for crop genetic improvement and models of food production for promoting sustainable *** momentous achievements and global deliveries of GSR have been fueled by the integration of abundant genetic resources,functional gene discoveries,and innovative breeding techniques with precise gene and whole-genome selection and efficient agronomic management to promote resource-saving,environmentally friendly crop production *** also provide perspectives on new horizons in genomic breeding technologies geared toward delivering green and nutritious crop varieties to further enhance the development of green agricul-ture and better nourish the world population.

关键词： rice functional genomics Green Super Rice genomic breeding nutritious crops sustainable agriculture

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郯庐断裂带起源机制的探讨

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矿物岩石地球化学通报 2016年第6期35卷 1120-1140页

作者：赵田朱光向必伟林少泽顾承串安徽大学资源与环境工程学院合肥230039 合肥工业大学资源与环境工程学院合肥230009 重庆大学土木工程学院重庆400044

为探讨郯庐断裂带的起源方式,对其印支期变形构造进行了详细的构造研究与综合分析。张八岭隆起带上张八岭群及肥东杂岩表现为上盘向南南西运动的平缓韧性滑脱变形带;庐江地区张八岭群表现为郯庐陡立的左行走滑剪切带;大别造山带东缘郯... 详细信息

为探讨郯庐断裂带的起源方式,对其印支期变形构造进行了详细的构造研究与综合分析。张八岭隆起带上张八岭群及肥东杂岩表现为上盘向南南西运动的平缓韧性滑脱变形带;庐江地区张八岭群表现为郯庐陡立的左行走滑剪切带;大别造山带东缘郯庐剪切带经历了两期变形事件,早期变形表现为北东走向左行走滑韧性剪切带,而晚期变形为叠加的北东-南西向紧闭褶皱;大别造山带内宿松杂岩及造山带南缘张八岭群表现为折返构造特征。结合前人白云母^(40)Ar/^(39)Ar定年结果及郯庐断裂带周缘前陆变形带特征,提出了郯庐断裂带起源的板片撕裂断层模式。

关键词：郯庐断裂带起源张八岭群左行走滑剪切带中三叠世板片撕裂断层

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安徽省部分地区马肠道寄生虫感染情况调查

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安徽科技学院学报 2020年第6期34卷 18-21页

作者：张慧郑克雷李惠虹薄震宇张振远刘欣超顾有方李文超安徽科技学院动物科学学院动物营养调控与健康安徽省重点实验室安徽凤阳233100

目的:调研安徽省部分地区马肠道寄生虫感染状况。方法:从安徽省多地的5家马场共采集163份新鲜马粪便样品,采用直接涂片法、饱和蔗糖溶液漂浮法、水洗沉淀法和卢戈氏碘液法等对上述样品进行检测,并根据所检获虫卵/卵囊/包囊的大小和形状... 详细信息

目的:调研安徽省部分地区马肠道寄生虫感染状况。方法:从安徽省多地的5家马场共采集163份新鲜马粪便样品,采用直接涂片法、饱和蔗糖溶液漂浮法、水洗沉淀法和卢戈氏碘液法等对上述样品进行检测,并根据所检获虫卵/卵囊/包囊的大小和形状等初步鉴别寄生虫虫种。结果:安徽省马肠道寄生虫总感染率为10.43%,共鉴别出圆线虫、毛线虫和结肠小袋纤毛虫3种虫种,其中毛线虫为感染的优势虫种,感染率为7.98%。明光市马肠道寄生虫感染率高达100.00%,怀远县和宿松县马场在本次调查中未发现寄生虫感染。结论:安徽省马肠道寄生虫总体防控较好,不同地区的感染情况具有一定差异,其中毛线虫的感染率最高,建议马场后期加强关于马肠道毛线虫的防控。

关键词：马肠道寄生虫调查安徽省

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