基于多视角时间序列图像的植物叶片分割与特征提取

为了解决多种类植物在生长过程中不同时间点动态变化表型参数提取困难问题,提出了一种基于多视角时间序列图像和深度卷积神经网络Mask-RCNN的植物茎叶实例分割方法,在拟南芥、玉米和酸浆属3种代表性植物上进行了实验.结果 表明,训练得到的基于Mask-RCNN的植物分割模型对在不同生长时期的植物茎叶的识别精度(mAP0.5...     

基于RGB-D相机的单株玉米株高测量方法

玉米株高是反映作物长势的重要指标。为了实现田间单株玉米株高的快速测量,提出了一种基于RGB-Depth（RGB-D）相机的玉米株高测量方法。以拔节期玉米为观测对象,首先利用RGB-D相机获取田间玉米的彩色图像和深度图像。对玉米彩色图像进行灰度化、二值化和去噪处理,提取出包含待测玉米的二值图像。利用改进的分水岭分割算法对玉米的灰度图像进行分割,对分割结果进行圆形拟合操作,定位玉米的中心区域。对玉米的二值图像进行骨架化处理,检测骨架的交叉点和末端点,确定玉米骨架的中心点,并检索其到末端点的最短路径。对各条路径的点云数据进行求差与比较,确定玉米的最高点,并对最高点附近的点云数据进行直方图统计,获得地面点。最后,通过计算玉米最高点和地面点的差值,实现单株玉米株高参数的测量。对玉米样本进行测试试验的结果表明：单株玉米株高的平均测量误差为1.62cm,均方根误差（RMSE）为1.86cm,测量精度满足实用要求。     

Solanum habrochaites introgression line grafted as rootstock in cultivated tomato maintains growth and improves yield under cold and drought stresses

Grafting tomato (Solanum lycopersicum L.) onto introgression lines (ILs) derived from S. habrochaites with introgression of a quantitative trait locus (QTL), stm9, for shoot turgor maintainer located on chromosome 9 has been suggested for improving yield under abiotic stresses. However, the physiological and agronomic responses of grafts with IL rootstocks to low root-zone temperature (RZT) and drought stresses are not yet clearly understood. Therefore, recurrent parent (RP) grafted onto IL and donor (D) rootstocks, and self-grafted IL and RP were examined at different combinations of optimal (20°–26°C RZT and well-watered) and suboptimal (10°C RZT and well-watered, and 20°–26°C RZT and cyclic drought) temperatures. Graft combinations were compared with self-grafted controls for dry weights, stomatal conductance (g_s), leaf parameters, osmotic adjustment, and stress tolerance index. The RP grafted onto IL rootstocks regulated g_s efficiently, retained the green trait, and produced higher biomass than the self-grafted RP. The IL rootstocks improved tolerance of the scion to drought and low RZT. Potential of IL rootstocks for improving tomato production under stressed conditions is discussed.     

Combined Drought and Heat Stress in Rice: Responses,Phenotyping and Strategies to Improve Tolerance

Simultaneous occurrence of drought and heat stress will have significant negative impact on rice yield, especially under upland conditions. The projected increase in global temperatures and reduced precipitation will increase the frequency of occurrence and intensity of these stresses, threatening rice production. Despite recognizing the importance of combined stress in rice, the knowledge generated in this area is very limited. Though complex, understanding combined stress tolerance of rice under water saving cultivation is more critical towards development of climate resilient rice cultivars. Here, we summarized the effects of combined stress on rice physiology with more emphasis on reproductive stage. Omics responses, phenotyping and physiology challenges and potential strategies for improving combined stress tolerance in rice are also discussed.     

Development of a high-throughput field phenotyping rover optimized for size-limited breeding fields as open-source hardware

Phenotyping is a critical process in plant breeding, especially when there is an increasing demand for streamlining a selection process in a breeding program. Since manual phenotyping has limited efficiency, high-throughput phenotyping methods are recently popularized owing to progress in sensor and image processing technologies. However, in a size-limited breeding field, which is common in Japan and other Asian countries, it is challenging to introduce large machinery in the field or fly unmanned aerial vehicles over the field. In this study, we developed a ground-based high-throughput field phenotyping rover that could be easily introduced to a field regardless of the scale and location of the field even without special facilities. We also made the field rover open-source hardware, making its system available to public for easy modification, so that anyone can build one for their own use at a low cost. The trial run of the field rover revealed that it allowed the collection of detailed remote-sensing images of plants and quantitative analyses based on the images. The results suggest that the field rover developed in this study could allow efficient phenotyping of plants especially in a small breeding field.     

Phenotyping of productivity and resilience in sweetpotato under water stress through UAV-based multispectral and thermal imagery in Mozambique

Sweetpotato is a crucial crop to guarantee food security in sub-Saharan Africa, and drought events are considered one of the most critical factors affecting sweetpotato productivity in this region. In this study, airborne imagery based on reflectance (NDVI, CI_red-edge) and canopy temperature minus air temperature (dT) indices was used to characterize sweetpotato genotypes under drought treatments in Mozambique. Two field experiments established in rainy/hot (Trial A) and dry/cool (Trial B) seasons were assessed. In Trial A, 24 genotypes were subjected to early- (ESD), mid- (MSD) and late-season (LSD) drought stress treatments and compared against a control. In Trial B, 120 genotypes were subjected to LSD only. The percentage of reduction in vine weight (PR_VW) under drought was related primarily to temporal variation of NDVI and CI, regardless of drought treatment and seasons. dT in relation to control (dT_Amp) was associated with PR_VW in ESD-Trial A and LSD-Trial B, whereas under LSD-Trial A, dT_Amp was related to total fresh storage root weight (TRW). During the rainy/hot season, higher TRW reduction was promoted under ESD; however, under LSD, it was possible to identify productive genotypes able to withstand drought stress, highlighting their relevance for drought-tolerance selection purposes.     

植物表型平台与图像分析技术研究进展与展望

近年来,植物基因组得到迅猛发展,但因缺乏足够的表型数据而限制了人类解析数量性状遗传学的能力。通过开发植物表型信息采集平台和进行图像分析可以加以解决。高通量、自动化、高分辨率的植物表型信息采集平台与分析技术对于加快植物改良和育种、提高产量和抗病虫害能力至关重要。将植物表型平台信息采集平台与分析技术用于解析基因组信息,定量研究与生长、产量和适应生物或非生物胁迫相关的复杂性状,是建立植物生长模型和采集农作物高维、丰富表型数据集的重要途径,能够满足填补基因组信息与植物表型可塑性之间空白的需要。阐述了基于光学成像的植物表型信息采集平台与图像分析技术的研究进展,从室内、田间不同的使用环境出发,根据不同搭载方式,总结分析了各表型平台的功能和特点。最后,分析了目前植物表型信息采集平台与分析技术存在的瓶颈问题,提出了以下建议与展望:开发植物表型信息采集平台的多传感器集成系统;将植物生长环境监测模块融入植物表型信息采集平台中;开发针对林木的表型信息采集平台;对传感器获取的表型数据进行更好的集成与挖掘;采用无损原位根系信息采集技术得到植物地下部分的表型数据;构建表型数据统一开放的标准,进行学科交叉的深度合作。     

Breeding banana (Musa spp.) for drought tolerance: A review

Drought is a major abiotic stress affecting banana production worldwide, leading to yield losses of up to 65%. Consequently, numerous efforts to understand and mitigate drought effects that include developing tolerant crop varieties are ongoing in several banana breeding programmes. The breeding efforts, however, have been greatly slowed down by inherent banana problems (polyploidy and male or female sterility) and complexity of drought tolerance (reportedly controlled by several genes). This review summarizes the pertinent research findings on water requirements of banana for its proper growth and productivity, symptoms of drought-sensitive varieties and field management strategies to cope with drought stress. The coping strategies deployed by resistant cultivars include high assimilation rates and water retention capacity as well as minor losses in leaf area and gaseous exchange. Reduced bunch weight, leaf chlorosis, wilting and strangled birth are underlined to be directly associated with drought susceptibility. Integration of conventional, molecular breeding and biotechnological tools as well as exploitation of the existing banana genetic diversity presents a huge opportunity for successful banana improvement.     

Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi-trait pedigree and genomic selection models

Training populations for pedigree and genomic prediction in plant breeding programmes are largely updated with superior genotypes from multi-environment trials, where they are tested with the goal of variety development. Such a selective phenotyping has, however, a negative impact on prediction abilities, especially when only a subset of breeding lines can be tested, as for laborious and costly to phenotype traits. This study focused, thus, on investigating the impact of selective phenotyping in the training population of an applied wheat breeding programme, and assessing the potential to mitigate this impact by pedigree and genomic multi-trait prediction models as well as bi-directionally selected training populations for several baking quality parameters. Combining both pedigree and genomic information in multi-trait prediction models with pre-existing phenotypic information for protein content and sedimentation value compensated for the observed loss in prediction ability, while entering few inferior breeding lines into the training population further mitigated the impact of selective phenotyping and even led to a slight increase of prediction ability in comparison to a randomly chosen training population.