Shubham Agarwal, Ofek Nourian, Michael Sidorov
et al.
Understanding plant root systems is critical for advancing research in soil-plant interactions, nutrient uptake, and overall plant health. However, accurate imaging of roots in subterranean environments remains a persistent challenge due to adverse conditions such as occlusion, varying soil moisture, and inherently low contrast, which limit the effectiveness of conventional vision-based approaches. In this work, we propose a novel underground imaging system that captures multiple overlapping views of plant roots and integrates a deep learning-based Multi-Image Super Resolution (MISR) framework designed to enhance root visibility and detail. To train and evaluate our approach, we construct a synthetic dataset that simulates realistic underground imaging scenarios, incorporating key environmental factors that affect image quality. Our proposed MISR algorithm leverages spatial redundancy across views to reconstruct high-resolution images with improved structural fidelity and visual clarity. Quantitative evaluations show that our approach outperforms state-of-the-art super resolution baselines, achieving a 2.3 percent reduction in BRISQUE, indicating improved image quality with the same CLIP-IQA score, thereby enabling enhanced phenotypic analysis of root systems. This, in turn, facilitates accurate estimation of critical root traits, including root hair count and root hair density. The proposed framework presents a promising direction for robust automatic underground plant root imaging and trait quantification for agricultural and ecological research.
Heesup Yun, Isaac Kazuo Uyehara, Ioannis Droutsas
et al.
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters and nested structures for these models at the field scales remains prohibitively labor-intensive. We present a novel algorithm that generates a 3D plant architecture from an image, creating a functional structural plant model that reflects organ-level geometric and topological parameters and provides a more comprehensive representation of the plant's architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we proposed a method that generates token sequences that encode a procedural definition of plant architecture. This work used only synthetic images for training and testing, with exact architectural parameters known, allowing testing of the hypothesis that organ-level architectural parameters could be extracted from image data using a vision-language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. The model achieved a token F1 score of 0.73 during teacher-forced training. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. This led to the conclusion that such plant architecture model generation and parameter extraction were possible from synthetic images; thus, future work will extend the approach to real imagery data.
Maristella Mastore, Elisa Broggio, Davide Banfi
et al.
The increase in the world population and consequent rise in food demand have led to the extensive use of chemical pesticides, causing environmental and health concerns. In response, biological control methods, particularly those involving microbial agents, have emerged as sustainable alternatives within integrated pest management. This study highlights the potential of <i>Lysinibacillus fusiformis</i> as a biocontrol agent against the dipteran <i>Drosophila suzukii</i> (Matsumura) (Diptera: Drosophilidae), a pest responsible for damaging soft-skinned fruits. Experimental treatments using vegetative cells, spores, and secondary metabolites of <i>L. fusiformis</i> on <i>D. suzukii</i> larvae demonstrated significant larvicidal effects, accompanied by observable changes in gut morphology under microscopy. Moreover, preliminary immunological assays showed the interference of this bacterium with the host immune system. All the results indicate the suitability of <i>L. fusiformis</i> for its possible integration into sustainable agricultural practices, although additional research is required to understand its applicability in the field.
The Yunnan-Kweichow Plateau (YKP), a representative ecologically fragile zone, is subject to dual pressures from intensified climate change and anthropogenic activities. The specific mechanisms of how Net Ecosystem Productivity (NEP) responds to these changes remain unclear, whose relative contributions remain poorly quantified. This study conducts spatiotemporal quantification analysis of NEP dynamics and influencing factors in the YKP from 2001 to 2020, which integrated a linear regression, shifting center of gravity, Mann-Kendall trend test, partial correlation analysis, and random forest. The results showed an enhancement in NEP within the YKP (slope = 2.42 g C·m−2·yr−1, p < 0.05). Overall, climate change and anthropogenic activities contributed 1.86 g C·m−2·yr−1 and 0.76 g C·m−2·yr−1 to NEP variations, respectively. In terms of climate impact, temperature and precipitation are the main drivers affecting vegetation change, while radiation has the least influence. The importance of precipitation on NEP has been increasing by an upward trend, particularly in non-humid regions (slope = 0.31, p < 0.05) and grassland (slope = 0.45, p < 0.05). Besides, although the impact of climate change is dominant throughout the region, in areas affected by anthropogenic activities and climate change, the influence of anthropogenic activities is dominant and has a positive impact on the vegetation growth of YKP and NEP, especially in forest areas. The research elucidates the coupling mechanisms of how anthropogenic activities and climate change drive vegetation dynamics in the YKP region, providing key insights for boosting carbon sink capacity and promoting ecological sustainability.
Christina Trujillo Frede, Ina Danquah, Thomas Friedrich
et al.
Abstract Human health is fundamental to the lives of individuals and societies. The dramatic decline in biodiversity is one of the most fundamental threats to the foundations of human life. Collected, cultivated and purchased plants have traditionally been used for their therapeutic, preventive or palliative effects on human health and well-being, and are deeply intertwined with culture and history. The IPBES report on the sustainable use of wild species in 2022 highlights the importance and contribution of the use of wild plants to global biodiversity conservation. However, there is still little reliable knowledge about the exact relationship between biodiversity and human health. In this paper, we argue that the example of knowledge and use of medicinal plants could be very well suited for a better understanding of the relations and interactions between biodiversity and health. From the perspective of Social Ecology, we advocate an inter- and transdisciplinary research approach that can provide both: system knowledge and transformation knowledge. We present the results of a conceptual study on knowledge and practices of medicinal plants in relation to their impact on human health and biodiversity in Germany. Community gardening, sustainable wild collection practices, and sharing of valuable local traditional knowledge can be possible pathways to conserve plant populations and knowledge. We argue that the practices around medicinal plants can create a reciprocal relationship between humans and these plants, leading to increased well-being and appreciation for them. To fully understand the impact of medicinal plants on health and biodiversity, it is necessary to move beyond a knowledge-focused analysis that has dominated literature to date and analyse the practices, benefits, and relationships between people, medicinal plants, and their ecosystems.
This paper presents a comprehensive analysis of power plant performance using the inverse Gaussian (IG) distribution framework. We combine theoretical foundations with practical applications, focusing on both combined cycle and nuclear power plant contexts. The study demonstrates the advantages of the IG distribution in modeling right-skewed industrial data, particularly in power generation. Using the UCI Combined Cycle Power Plant Dataset, we establishthe superiority of IG-based models over traditional approaches through rigorous statistical testing and model validation. The methodology developed here extends naturally to nuclear power plant applications, where similar statistical patterns emerge in operational data. Our findings suggest that IG-based models provide more accurate predictions and better capture the underlying physical processes in power generation systems.
Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predictsfour key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.
Growth and wood traits of Leucaena leucocephala subsp. glabrata were compared with India's most widely grown pulpwood, Eucalyptus. Three Leucaena seedlots and sixty-four Leucaena clones selected in plantations established from two Indian land races and a Hawaiian seed source were tested together with five commercial E. camaldulensis clones at two contrasting sites in southern India. At the wetter, irrigated site, the three sources of Leucaena clones had significantly greater 4-year height and similar mean stem diameter to the Eucalyptus clones, while the Leucaena seedlots were slower-growing. At the semi-arid, rainfed site, Eucalyptus grew faster and had better survival than the three sources of Leucaena clones, which again were similar in their performance and superior to the Leucaena seedlots. Basic densities, determined from breast-height wood cores, of 4-year-old Leucaena and Eucalyptus were quite similar (568 and 534 kg m-3 respectively) at the irrigated site, but at the rainfed site Eucalyptus wood density was higher (590 kg m-3), while that of Leucaena was lower (508 kg m-3). The kraft pulp yield (KPY) of Leucaena clones was about 1.5 % higher than Eucalyptus at both sites; KPYs of both species were 1.8 % lower at the rainfed than the irrigated site. Leucaena fibres were 31 % and 39 % longer and 65 % and 58 % wider than Eucalyptus, at the irrigated and rainfed sites respectively, and had lower cell wall proportions. Differences among Leucaena clones were significant for growth traits and for most wood and fibre traits, indicating the potential for selecting fast-growing Leucaena clones with improved wood properties. Clonal Leucaena plantations can serve as a productive and complementary pulpwood crop to Eucalyptus in southern India, increasing the yield and strength of blended pulps.
Kriti Agarwal, Samhruth Ananthanarayanan, Srinitish Srinivasan
et al.
This paper presents the development of a novel plant communication application that allows plants to "talk" to humans using real-time sensor data and AI-powered language models. Utilizing soil sensors that track moisture, temperature, and nutrient levels, the system feeds this data into the Gemini API, where it is processed and transformed into natural language insights about the plant's health and "mood." Developed using Flutter, Firebase, and ThingSpeak, the app offers a seamless user experience with real-time interaction capabilities. By fostering human-plant connectivity, this system enhances plant care practices, promotes sustainability, and introduces innovative applications for AI and IoT technologies in both personal and agricultural contexts. The paper explores the technical architecture, system integration, and broader implications of AI-driven plant communication.
Luise Wraase, Victoria M. Reuber, Philipp Kurth
et al.
Abstract Subterranean animals act as ecosystem engineers, for example, through soil perturbation and herbivory, shaping their environments worldwide. As the occurrence of animals is often linked to above‐ground features such as plant species composition or landscape textures, satellite‐based remote sensing approaches can be used to predict the distribution of subterranean species. Here, we combine in‐situ collected vegetation composition data with remotely sensed data to improve the prediction of a subterranean species across a large spatial scale. We compared three machine learning‐based modeling strategies, including field and satellite‐based remote sensing data to different extents, in order to predict the distribution of the subterranean giant root‐rat GRR, Tachyoryctes macrocephalus, an endangered rodent species endemic to the Bale Mountains in southeast Ethiopia. We included no, some and extensive fieldwork data in the modeling to test how these data improved prediction quality. We found prediction quality to be particularly dependent on the spatial coverage of the training data. Species distributions were best predicted by using texture metrics and eyeball‐selected data points of landscape marks created by the GRR. Vegetation composition as a predictor showed the lowest contribution to model performance and lacked spatial accuracy. Our results suggest that the time‐consuming collection of vegetation data in the field is not necessarily required for the prediction of subterranean species that leave traceable above‐ground landscape marks like the GRR. Instead, remotely sensed and spatially eyeball‐selected presence data of subterranean species could profoundly enhance predictions. The usage of remote sensing‐derived texture metrics has great potential for improving the distribution modeling of subterranean species, especially in arid ecosystems.
Raquel Jiménez-Melero, Patricio Bohorquez, Inmaculada González-Planet
et al.
Mediterranean temporary ponds are a priority habitat according to the Natura 2000 network of the European Union, and complete inventories of these ecosystems are therefore needed. Their small size, short hydroperiod, or severe disturbance make these ponds undetectable by most remote sensing systems. Here we show, for the first time, that the distributed hydrologic model IBER+ detects ephemeral and even extinct wetlands by fully exploiting the available digital elevation model and resolving many microtopographic features at drainage basin scales of about 1000 km<sup>2</sup>. This paper aims to implement a methodology for siting flood-prone areas that can potentially host a temporary wetland, validating the results with historical orthophotos and existing wetlands inventories. Our model succeeds in dryland endorheic catchments of the Upper Guadalquivir Basin: it has detected 89% of the previously catalogued wetlands and found four new unknown wetlands. In addition, we have found that 24% of the detected wetlands have disappeared because of global change. Subsequently, environmental managers could use the proposed methodology to locate wetlands quickly and cheaply. Finding wetlands would help monitor their conservation and restore them if needed.
Mozzamil Mohammed, Åke Brännström, Pietro Landi
et al.
Plant-frugivore interactions play a central role for plant persistence and spatial distribution by promoting the long-range dispersal of seeds by frugivores. However, plant-frugivore interactions are increasingly being threatened by anthropogenic activities. An important anthropogenic threat that could expose plant-frugivore systems to extinction risk is fruit harvesting. Here, we develop an individual-based and a pair-approximation model of plant-frugivore-human interactions to elucidate the effects of human harvesting of fruits on plant establishment, persistence, and spatial distribution. Our results show that frugivores strongly affect global density of plants and gradually shift their spatial distribution from aggregated to random, depending on the attack rate and dispersal efficiency of frugivores. We find that, in the absence of frugivores, plants experiencing intense fruit harvesting cannot persist even if their fecundity is high. In the presence of frugivores, fruit harvesting profoundly affects the global dispersal of seeds and thus changes the spatial distributions of plants from random to aggregated, potentially causing plant extinction. Our results demonstrate that sufficiently efficient frugivores mitigate the negative impact of fruit harvesting on plant populations and enable plant establishment precluded by harvesting. Taken together, these results draw attention to previously underappreciated impacts of fruit harvesting in plant-frugivore-human interactions.
The stability of ecological systems is a fundamental concept in ecology, which offers profound insights into species coexistence, biodiversity, and community persistence. In this article, we provide a systematic and comprehensive review on the theoretical frameworks for analyzing the stability of ecological systems. Notably, we survey various stability notions, including linear stability, sign stability, diagonal stability, D-stability, total stability, sector stability, structural stability, and higher-order stability. For each of these stability notions, we examine necessary or sufficient conditions for achieving such stability and demonstrate the intricate interplay of these conditions on the network structures of ecological systems. Finally, we explore the future prospects of these stability notions.
In this report, I will review some of the most used models in theoretical ecology along with appealing reformulations and recent results in terms of diversity, stability, and functioning of large well-mixed ecological communities.