This paper develops an end-to-end odor communication model for stress signaling between plants using Green Leaf Volatiles (GLV). A damaged transmitter plant emits (Z)-3-hexenal, (Z)-3-hexenol, and (Z)-3-hexenyl acetate, which propagate through a time-varying diffusion-advection channel and undergo multiplicative loss at the receiver. The sink plant is modeled with a biochemical receiver network that converts the received GLVs into the defensive metabolite (Z)-3-hexenyl $β$-vicianoside, and an alarm decision is defined based on its concentration level. Numerical results show that (Z)-3-hexenol is the primary driver of the system and that plant perception generally operates in a non-linear region. These findings provide a framework for understanding the evolution of plant-plant communication and for developing next-generation precision farming technologies.
Aneta Bylak, Andrzej Bobiec, Mateusz Bobiec
et al.
Abstract The pool of invasive ornamental plants keeps expanding, and one of the best studied plant invasion habitats is the riparian zone. Europe has no native Miscanthus spp. or bamboos, which are popular garden plants. In 2022–2024 we observed Bisset bamboo (Phyllostachys bissetii) and giant miscanthus (Miscanthus × giganteus) naturalizing in the riparian zones of two rivers of the Vistula River basin (Poland). Bisset bamboo has not been recorded before in the wild in Europe and giant miscanthus has not been reported before as naturalized in Europe. We describe their present habitats and invasive potential, to alert others to the prospect of spread in Europe. Examples from other parts of world indicate that Phyllostachys spp. invasive running bamboo has a tendency to spread aggressively. Because we only located single plants our species qualify as ‘casuals’, but we mention them out of a concern that these species are establishing more widely or will soon do so. Our observations fit an “accelerated trend” in exotic plant invasion in Europe, in particular, of escaped ornamental plants. Based on information about the ecology of both species, their popularity in horticulture, and our observations, we speculate that giant miscanthus and Bisset bamboo may become new European plant invaders. Both species should be mechanically removed. There is an urgent need to raise awareness among gardeners, hobbyists, plant sellers and importers, about environmental risk from spread of invasive plants. It is concerning that seedlings and seeds of other species of the genera Miscanthus and Phyllostachys, which have naturalised in several European countries, are available in horticulture. Bioinvasion is easier to control if there is early detection and a rapid response.
In the face of global warming, drought is becoming an increasingly severe issue and has emerged as a crucial factor affecting agricultural production. Investigating the effects of drought stress on the growth, development, and physiological traits of rice, elucidating the underlying response mechanisms of rice to drought stress, and exploring agronomic practices to reduce yield losses under such stress are essential for boosting rice yields in arid conditions and safeguarding global food security. This review comprehensively synthesizes the latest research progress on the changes in the growth, development, and physiological characteristics of rice under drought stress, as well as the regulatory mechanisms of drought tolerance. It delves deeply into the stress responses and energy metabolism patterns of rice induced by drought stress. Moreover, it systematically summarizes the establishment of drought tolerance evaluation systems and the screening methods for drought-tolerant rice varieties. At the same time, it outlines practical agronomic measures and management strategies for combating drought stress, aiming to provide a scientific basis for rice cultivation in drought-affected regions.
Photosynthesis is vital for the survival of entire ecosystems on Earth. While light is fundamental to this process, excessive exposure can be detrimental to plant cells. Chloroplasts, the photosynthetic organelles, actively move in response to light and self-organize within the cell to tune light absorption. These disk-shaped motile organelles must balance dense packing for enhanced light absorption under dim conditions with spatial rearrangements to avoid damage from excessive light exposure. Here, we reveal that the packing characteristics of chloroplasts within plant cells show signatures of optimality. Combining measurements of chloroplast densities and three-dimensional cell shape in the water plant Elodea densa, we construct an argument for optimal cell shape versus chloroplast size to achieve two targets: dense packing into a two-dimensional monolayer for optimal absorption under dim light conditions and packing at the sidewalls for optimal light avoidance. We formalize these constraints using a model for random close packing matched with packing simulations of polydisperse hard disks confined within rectangular boxes. The optimal cell shape resulting from these models corresponds closely to that measured in the box-like plant cells, highlighting the importance of particle packing in the light adaptation of plants. Understanding the interplay between structure and function sheds light on how plants achieve efficient photo adaptation. It also highlights a broader principle: how cell shape relates to the optimization of packing finite and relatively small numbers of organelles under confinement. This universal challenge in biological systems shares fundamental features with the mechanics of confined granular media and the jamming transitions in dense active and passive systems across various scales and contexts.
Plant disease is a critical factor affecting agricultural production. Traditional manual recognition methods face significant drawbacks, including low accuracy, high costs, and inefficiency. Deep learning techniques have demonstrated significant benefits in identifying plant diseases, but they still face challenges such as inference delays and high energy consumption. Deep learning algorithms are difficult to run on resource-limited embedded devices. Offloading these models to cloud servers is confronted with the restriction of communication bandwidth, and all of these factors will influence the inference's efficiency. We propose a collaborative inference framework for recognizing plant diseases between edge devices and cloud servers to enhance inference speed. The DNN model for plant disease recognition is pruned through deep reinforcement learning to improve the inference speed and reduce energy consumption. Then the optimal split point is determined by a greedy strategy to achieve the best collaborated inference acceleration. Finally, the system for collaborative inference acceleration in plant disease recognition has been implemented using Gradio to facilitate friendly human-machine interaction. Experiments indicate that the proposed collaborative inference framework significantly increases inference speed while maintaining acceptable recognition accuracy, offering a novel solution for rapidly diagnosing and preventing plant diseases.
Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid
et al.
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Simeon Adebola, Shuangyu Xie, Chung Min Kim
et al.
Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/
Cross-commodity valuation approaches to value gas fire power plants are well studied in the literature. Hence, the value of the gas fire power plant is identical to the value of a spark spread option wherein the underlying are electricity and gas with a strike price assimilated to operating and maintenance costs. Power and fuels spot prices account for uncertain futures cash-flows for power-plant generator owners. For instance, for gas-fired turbine plant, spot prices of electricity and gas determine the random cash-flows of the power-plant. Other than the spot prices, the valuation of such plant involves among other deterministic cost the plant heat rate and operating costs. Recently, the cost of emissions is considered into the valuation to tackle environmental issues. Given some simplifications in the plant cash-flow modelling, the value of such plant can either be expressed as the price of i) a cross-commodity option or ii) the price of a real option. Here, we focus on cross-commodity option valuation approach where the value of the power plant is approached as the value of a spark spread option. When spot prices of the underlying commodities are log-normal, closed formulae or approximations can be obtained using Kirk's approximation. Naturally, the spot price of electricity and gas present spikes due to seasonality among other factors. However, in that case it is not possible to get a closed formula for the spark spread option. In this paper we explore possibilities to approximate spark spread options when spot prices fall into a class of jump diffusion processes.
چکیده مبسوط:سابقه و هدف: بهبود عملکرد محصولات زراعی از دو جنبه کمیتی و کیفیتی همواره مهمترین هدف اصلاحگران بوده است. در گندم، کیفیت نانوایی عمدتا تابع میزان و نوع پروتئینهای تشکیلدهنده گلوتن بویژه گلوتنین میباشد. هدف این مطالعه تعیین کیفیت نانوایی با استفاده از نشانگرهای STS مرتبط با زیرواحدهای با وزن مولکولی بالا گلوتنین (HMWG) و همچنین مقایسه عملکرد دانه و برخی صفات مورفولوژیک ژنوتیپهای گندم در استان گلستان بود. مواد و روشها: بذور 30 ژنوتیپ گندم شامل ارقام رایج و لاینهای امیدبخش گندم از طرح انتخاب ارقام مشارکتی استان گلستان در مزرعه دانشگاه علوم کشاورزی و منابع طبیعی گرگان در قالب یک طرح بلوک کامل در 3 تکرار کشت و در زمان رسیدگی بوتهها ارزیابی صفات زراعی صورت گرفت. در آزمایشگاه پس از استخراج DNA و رنگآمیزی محصولات PCR با استفاده از 10 جفت آغازگر STS، مجموع امتیاز زیرواحدهای HMWG برای هر ژنوتیپ ثبت و سپس به عنوان امتیاز کیفی آن نمونه مورد تجزیه و تحلیل قرار گرفت. یافتهها: تجزیه واریانس دادهها نشان دادکه تنوع معنیداری برای عملکرد دانه، ارتفاع بوته، طول سنبله، تعداد دانه در سنبله، تعداد سنبله در متر مربع و وزن هزار دانه وجود داشت. بیشترین عملکرد دانه در رقم نودل و لاینهای امیدبخش N93-9، N93-17، N92-19 و کراس5028 مشاهده شد. پلیمورفیسم قابلتوجهی برای زیرواحدهای HMWG در جایگاههای ژنی Glu-A1، Glu-B1 و Glu-D1 مشاهده گردید، بطوریکه امتیاز کیفی ژنوتیپها بین 6 تا 10 برآورد شد و 13 ژنوتیپ امتیاز کیفی حداکثر (10) را دریافت نمودند. اندازه باندهای بدست آمده برای آغازگرها و زیرواحدهای مشاهده شده در رقم چینی بهاره (نمونه شاهد) با نتایج سایر محققین مطابقت کامل داشت. تجزیه خوشهای بر مبنای امتیازات کیفی، ژنوتیپها را در چهار کلاستر مجزا دستهبندی نمود. این کلاسترها به ترتیب حاوی ژنوتیپهای دارای کیفیت نانوایی خوب، مطلوب، متوسط و ضعیف بودند. ارزشگذاری نمونهها از هر دو جنبه کمی و کیفی نشان داد که در بین مورد مطالعه 13 ژنوتیپ دارای عملکرد دانه و امتیاز کیفی بالا، 5 ژنوتیپ با عملکرد دانه بالا و امتیاز کیفی پایین، 10 ژنوتیپ دارای عملکرد دانه پایین و امتیاز کیفی بالا و 2 ژنوتیپ دارای عملکرد دانه و امتیاز کیفی پایین بودند. نتیجهگیری: نتایج نشان داد که در جامعه مورد بررسی واریانس برای عملکرد دانه و سایر صفات مورفولوژیک و همچنین پلیمورفیسم برای HMWG قابلتوجه بود. قرارگیری نودل، تیرگان و مروارید در گروه ارقام دارای عملکرد و کیفیت بالا، بیانگر ارزشمند بودن آنها به عنوان منبع ژنهای مطلوب بود. این مطالعه کارایی نشانگرهای STS در بهبود متوسط کیفیت نانوایی گندم و پتانسیل آنها برای MAS را اثبات نمود.
Transcriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020–2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE.
Anna L Erdei, Aneth B David, Eleni C Savvidou
et al.
Over two decades ago, an intercropping strategy was developed that received critical acclaim for synergizing food security with ecosystem resilience in smallholder farming. The push–pull strategy reportedly suppresses lepidopteran pests in maize through a combination of a repellent intercrop (push), commonly Desmodium spp., and an attractive, border crop (pull). Key in the system is the intercrop’s constitutive release of volatile terpenoids that repel herbivores. However, the earlier described volatile terpenoids were not detectable in the headspace of Desmodium, and only minimally upon herbivory. This was independent of soil type, microbiome composition, and whether collections were made in the laboratory or in the field. Furthermore, in oviposition choice tests in a wind tunnel, maize with or without an odor background of Desmodium was equally attractive for the invasive pest Spodoptera frugiperda. In search of an alternative mechanism, we found that neonate larvae strongly preferred Desmodium over maize. However, their development stagnated and no larva survived. In addition, older larvae were frequently seen impaled and immobilized by the dense network of silica-fortified, non-glandular trichomes. Thus, our data suggest that Desmodium may act through intercepting and decimating dispersing larval offspring rather than adult deterrence. As a hallmark of sustainable pest control, maize–Desmodium push–pull intercropping has inspired countless efforts to emulate stimulo-deterrent diversion in other cropping systems. However, detailed knowledge of the actual mechanisms is required to rationally improve the strategy, and translate the concept to other cropping systems.
Flavor and spice are largely consumed in food, cosmetics, and pharmaceutical industries. A novel coronavirus, recently named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was first identified in humans in Wuhan, China in 2019. This study is to examine whether flavor components can prevent humans from SARS-CoV-2 infection. Given that the drugable antiviral target ACE2 receptor and viral main protease (Mpro) were reported, 169 compounds were screened against these two targets by using autodock vina. According to our docking screening, 10 antiviral components, including glycyrrhizic acid, theaflavin 3,3'-digallate, agnuside, fenflumizole, angelicide, sageone, oleanic acid, benzyl (3-fluoro-4-morpholine-4-yl phenyl) carbamate, glycerol ester of rosin, and endere S can directly bind to both host cell target ACE2 receptor and viral target Mpro, indicating their potential for SARS-CoV-2 treatment. In addition, experimental verification found that theaflavin 3,3'-digallate show significant inhibit Mpro/3CLpro activity.
Aimen Shafique, Riffat Batool, Muhammad Rizwan
et al.
Potassium is a monovalent cation with an osmotic activity in plants. It comprises 10 to 11% of plant dry weight and has crucial importance in the development and stress mechanism of plants. A well-studied system of K+ channels and transporters is involved in the regulation and transport of potassium from soil to different parts of plants. However, the knowledge of this system in ornamental plants especially in roses is limited. In current research, various omics approaches were utilized to characterize the K+ transport system in China rose (Rosa chinensis). Genomewide analysis revealed that a total of 32 genes were candidates for K+ channels (21) and transporters (11). Based on their conserved domain and motif, these genes were further classified into sub-families as K+ channels: 6 Shakers, 5 TPKs, and K+ transporters: 2 HKTs, 5 KEAs, and 14 KUP/HAK/KTs. Phylogenetic and evolutionary studies revealed that segmental duplication may play an important role in the expansion of this gene family. The cis-elements in promoter region showed that these K+ transport-related genes may be involved in response to various abiotic stresses. RNA-seq analysis and its validation through qRT-PCR showed that a channel gene RcAKT1.2 and two transporter genes RcKUP/HAK/KT11 and RcKUP/HAK/KT13 were potentially involved in the regulation of plant stress response. This research explains the valuable vision for functional assays of K+ transport system in China rose.
The present study was conducted during September-October, 2022 at Greenhouse facility of Experimental Farm, Faculty of Agriculture Sciences, Mandsaur University, Mandsaur, Madhya Pradesh, India to identify drought tolerant genotypes. Total sixty genotypes were sown on dated 02/09/2022 by using polythene bags in completely randomized block design with four different water regimes and two replications in each set during month of September, 2022. Data were recorded for root-shoot parameters and relative leaf water content. Mean performance of root length showed that among 60 genotypes, thirty genotypes showed increased tap roots under severe water deficit conditions (0%). Under sever water stress condition (0%) highest root length was recorded of genotypes NRC138 (20.5 cm) followed by GW251 (18.15 cm) and RSC1107 (17.2 cm) respectively while lowest root length was noted in accession GW312 (3.4 cm) followed by NRC37 (5 cm) and NRC 142 (5.15 cm) respectively. Under0% water stress highest relative leaf water content was observed in genotype JS2034 (97.16%) followed by GW10 (93.47%) and GW159 (89.18%) whereas lowest was found in GW28 (20.96%) followed by GW100 (26.31%) and AGS25 (26.66%). On basis of mean data of root length, relative leaf water content, root shoot ratio by length and visual observation of plants, 11 genotypes were identified as drought tolerant and 19 genotypes were identified as medium tolerant. The identified drought tolerant genotypes may be used as water stress tolerant genotypes in future for improvement of crop in relation to drought tolerance.
Vladimíra Dekanová, Milan Novikmec, Ivana Svitková
et al.
Leaf litter decomposition is a critical ecosystem-level process in many freshwater habitats. Although ponds are likely to derive a large proportion of their energy from riparian vegetation, allochthonous organic matter decomposition in these water bodies has received little attention. We studied the breakdown rates of black alder (Alnus glutinosa (L.) Gaertn.) litter in ponds and provide the first evidence of the role of the taxonomic and functional diversity of pond-dwelling shredders in this ecosystem process. Despite a strong connection to riparian zones, the litter breakdown rates observed in ponds were generally lower than those reported in headwater streams. It seems that ponds provide less favorable conditions for shredder communities than headwaters. The rate of organic matter decomposition in ponds was significantly positively related to functional diversity, represented by the variability of shredder body size, while shredder species richness did not appear to be a reliable proxy for this ecosystem function. This finding is consistent with theoretical predictions that functional complementarity among species has a systematic effect on ecosystem processes. It also emphasizes that body size is a crucial functional trait mediating the effects of shredder diversity on litter decomposition in ponds.
There are two main life cycles in plants, annual and perennial. These life cycles are associated with different traits that determine ecosystem function. Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here, we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced data points to map the worldwide biogeography thereof. We found that annuals are half as common as initially thought, accounting for only 6% of plant species. Our analyses indicate annuals are favored in hot and dry regions. However, a more accurate model shows annual species' prevalence is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than deserts. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favorability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world's ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennials, as ongoing changes are leading to a more annuals-dominated world.
Gordana Popovic, Tanya J. Mason, Tiago A. Marques
et al.
Increasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry-picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: 1. Define a focused research question, then plan sampling and analysis to answer it; 2. Develop a model that accounts for the distribution and dependence of your data; 3. Emphasise effect sizes to replace statistical significance with ecological relevance; 4. Report your methods and findings in sufficient detail so that your research is valid and reproducible. Listed in approximate order of importance, these principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well-defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to false positives and poor replicability. Correct and appropriate statistical models give sound conclusions, good reporting practices and a focus on ecological relevance make results impactful and replicable. Illustrated with an example from a recent study into the impact of disturbance on upland swamps, this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods.
Plant disease recognition has witnessed a significant improvement with deep learning in recent years. Although plant disease datasets are essential and many relevant datasets are public available, two fundamental questions exist. First, how to differentiate datasets and further choose suitable public datasets for specific applications? Second, what kinds of characteristics of datasets are desired to achieve promising performance in real-world applications? To address the questions, this study explicitly propose an informative taxonomy to describe potential plant disease datasets. We further provide several directions for future, such as creating challenge-oriented datasets and the ultimate objective deploying deep learning in real-world applications with satisfactory performance. In addition, existing related public RGB image datasets are summarized. We believe that this study will contributing making better datasets and that this study will contribute beyond plant disease recognition such as plant species recognition. To facilitate the community, our project is public https://github.com/xml94/PPDRD with the information of relevant public datasets.
Akshay K. Burusa, Joost Scholten, David Rapado Rincon
et al.
Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.