High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops

Field phenotyping and long-term platforms to characterise how crop genotypes interact with soil processes and the environment

Unsustainable agronomic practices and environmental change necessitate a revolution in agricultural production to ensure food security. A new generation of crops that yield more with fewer inputs and are adapted to more variable environments is needed. However, major changes in breeding programmes may be required to achieve this goal. By using the genetic variation in crop yield in specific target environments that vary in soil type, soil management, nutrient inputs and environmental stresses, robust traits suited to specific conditions can be identified. It is here that long-term experimental platforms and field phenotyping have an important role to play. In this review, we will provide information about some of the field-based platforms available and the cutting edge phenotyping systems at our disposal. We will also identify gaps in our field phenotyping resources that should be filled. We will go on to review the challenges in producing crop ideotypes for the dominant management systems for which we need sustainable solutions, and we discuss the potential impact of three-way interactions between genetics, environment and management. Finally, we will discuss the role that modelling can play in allowing us to fast-track some of these processes to allow us to make rapid gains in agricultural sustainability

Archaeobotanical applications of microCT imaging

This thesis explores the ways in which the three-dimensional and non-destructive imaging technique of microCT can be applied to archaeobotanical materials to extract additional information previously inaccessible using traditional two-dimensional techniques. Across a series of eight publications, two microCT imaging protocols focusing on the imaging and analysis of two distinct types of archaeobotanical remains are presented along with archaeological case studies to which they have been successfully applied. Both protocols seek to utilise the relatively new imaging technique of microCT in order to explore the histories of some of the world's most important, yet in some cases understudied food crops including rice (Oryza sativa) in Island Southeast Asia, sorghum (Sorghum bicolor) and pearl millet (Pennisetum glaucum) in Africa, and taro (Colocasia esculenta), sweet potato (Ipomoea batatas), and yams (Dioscoreaceae) in Southeast Asia and the Pacific. The first protocol outlines how organic cereal tempers can be virtually extracted from inside pottery sherds through the use of microCT scanning and 3D digital segmentation techniques. These extracted digital remains can then be taxonomically identified and their domesticated status assessed using the morphological information only accessible with the penetrative X-rays of microCT. This protocol has been successfully applied to extract new rice and sorghum assemblages from previously excavated pottery sherds and their analysis has expanded our knowledge of the dispersal and early cultivation histories of these staple food crops. The second protocol uses microCT to build the first virtual reference collection of a greatly understudied type of archaeobotanical evidence, archaeological parenchyma. This protocol was developed by imaging samples of important root crops in the Southeast Asia and Pacific region from Jon Hather's parenchyma reference collection and applying his taxonomic identification method developed in the 1980s and 90s. Here his method is updated and adapted to include the added three-dimensional contextual information provided by microCT scanning as well as the greater range of anatomical variation captured both within and between species. The microCT datasets of these reference samples will form part of the first publicly accessible, online and virtual, archaeological parenchyma reference collection, which will hopefully encourage wider adoption and application of the technique. Both archaeobotanical microCT protocols presented here demonstrate the enormous potential of the technique to expand on our current sources of archaeobotanical evidence. The digital nature of the datasets presents the possibility of increasing analytical efficiency in the future with the development of automated archaeobotanical analyses

Investigating the Effect of Putative Cytokinin Antagonists on Root Growth in Rice, and their Efficacy in Mitigating Stress

There is a plethora of challenges that must be addressed this century to ensure the demand for food, fodder and biofuel is met. Feeding 9 billion people whilst counteracting the negative effects that erratic and more severe weather events are having due to climate change is a challenge that requires innovative approaches. Drought and salinity are significant limiting factors to crop yields, and modifying plant traits to avoid these stresses has been identified as a method of improving crop productivity. This study investigated the ability of putative root-specific cytokinin antagonists, molecules that block activity of the plant hormone cytokinin, to promote root growth in rice as a mechanism for improving crop abiotic stress tolerance. In addition to the parent compound, four novel compounds synthesised by Globachem Discovery Ltd. were found to promote root growth of the rice variety, Nipponbare, in liquid media. Subsequently, seed priming was established as a way of applying the compounds, significantly reducing preparation time and the quantity of product required. The long-term effects of priming were found to not affect aboveground biomass but did confer a negative effect to yield. The compounds were also tested for their ability to promote root growth in commercially relevant rice varieties and growth settings under drought and salt stress. However, the increase in root length found in Nipponbare was not observed in a commercial setting or commercially used rice varieties under optimum or stress conditions, highlighting the high specificity of the compounds. These findings show that whilst there is potential for these compounds to promote root growth, their use must be further optimised for agricultural purposes. In parallel to the lab-based studies, three models were designed and implemented in Chapters 2, 4 and 5. A machine learning technique was used to predict the likelihood of a compound having biological activity, based on its chemical properties. In a subsequent chapter the effects of spatial heterogeneity within a glasshouse were quantified and accounted for statistically. Finally, geospatial modelling was used to identify key regions where plant growth regulators could be applied most effectively. These models allow the optimisation of current practice, from agrochemical design to dissemination of a product, thereby contributing to a more robust agricultural system. The lab-based assays and different modelling approaches used in this study highlight the multi-faceted and collaborative approaches that are required to tackle the pressing humanitarian and environmental challenges of this century. This study goes some way to addressing these challenges

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

New Traits of Agriculture/Food Quality Interface

There is a close link between food and territory. The current challenges are located in precision agriculture and food metrology from the perspective of monitoring and improving food quality, and addressing the promotion of diversity of agroecosystems and diets. Research studies describing factors affecting food quality—such as agronomic conditions, post-harvest elicitors, cultivar selection, harvest date, or environmental influences—are welcome. Sustainable environmental and innovative practices should be promoted. Advanced techniques, such as mass spectrometry, infrared, and Raman spectroscopy in the monitoring and control of foodstuffs to model the agrofood system should be considered. Innovative green technologies should be taken into account. Targeting food approaches should be promoted. Chemometrics applications are welcome. This issue promotes highly interdisciplinary studies, including disciplines from agriculture and biology, chemistry, and nutrition. All types of articles, such as original research, opinions, and reviews, are welcome

Biomineralization

This open access book is the proceedings of the 14th International Symposium on Biomineralization (BIOMIN XIV) held in 2017 at Tsukuba. Over the past 45 years, biomineralization research has unveiled details of the characteristics of the nano-structure of various biominerals; the formation mechanism of this nano-structure, including the initial stage of crystallization; and the function of organic matrices in biominerals, and this knowledge has been applied to dental, medical, pharmaceutical, materials, agricultural and environmental sciences and paleontology. As such, biomineralization is an important interdisciplinary research area, and further advances are expected in both fundamental and applied research

InterDrought-V

Drought is weather-related natural disaster, which affects vast regions for months or years and has impact on food production. Drought is related to a deficiency of precipitation over a season or an extended period of time. The most immediate consequence is a fall in crop production, due to inadequate and poorly distributed rainfall. Given the severity of drought, a central challenge for researchers and policy makers is to device technologies that lend greater resilience to agricultural production under this stress. InterDrought conferences, in view of above, serve as a platform for presenting and debating key issues and strategies relevant for improving drought and other stress tolerance in crops. The main mission of the conference is to explore the possibilities of scientific and technological applications in crop improvement. In continuation of earlier InterDrought Conferences held in France (ID-I, 1995), Italy (ID-II, 2005), China (ID-III, 2009) and Australia (ID-IV, 2013), the next InterDrought Conference, InterDrought-V, is being organized in India..