Herve Goeau, Vincent Espitalier, Pierre Bonnet
et al.
Plot images are essential for ecological studies, enabling standardized sampling, biodiversity assessment, long-term monitoring and remote, large-scale surveys. Plot images are typically fifty centimetres or one square meter in size, and botanists meticulously identify all the species found there. The integration of AI could significantly improve the efficiency of specialists, helping them to extend the scope and coverage of ecological studies. To evaluate advances in this regard, the PlantCLEF 2024 challenge leverages a new test set of thousands of multi-label images annotated by experts and covering over 800 species. In addition, it provides a large training set of 1.7 million individual plant images as well as state-of-the-art vision transformer models pre-trained on this data. The task is evaluated as a (weakly-labeled) multi-label classification task where the aim is to predict all the plant species present on a high-resolution plot image (using the single-label training data). In this paper, we provide an detailed description of the data, the evaluation methodology, the methods and models employed by the participants and the results achieved.
Heightened awareness of monoculture cacao's negative impacts has led to the adoption of multifunctional landscape approaches aimed at optimizing ecological, social, and economic outcomes in cacao plantations. This study outlines multifunctional landscape plans for the Rongkong watershed, addressing both land and landscape scales to support sustainable and regenerative cacao development. The landscape planning includes agroforestry-based land-sharing scenarios and land-sparing approaches, evaluated based on the synergy and economic value of ecosystem services provided. The cacao-durian-avocado agroforestry combination stands out as the most advantageous, offering synergistic benefits in provisioning and regulating services, along with substantial financial gains. This scenario yields higher values for FNPV, ENPV, FBCR, and EBCR, achieving up to 2.23, 1.66, 1.77, and 1.51 times the value of monoculture cacao in upstream areas, and 2.26, 1.61, 1.78, and 1.42 times in downstream regions. Additionally, managing this combination through land-sparing methods allows 0.4 ha per ha of agricultural farm to be saved while delivering equivalent benefits. At a broader landscape level, the cacao-durian-avocado model is promising when scaled to replace monoculture cacao, traditional agroforestry, and marginal lands. Nevertheless, it's essential to recognize that variations in biophysical and socioeconomic conditions can influence differences in land equivalent ratio (LER), net present value (NPV), and benefit-cost ratio (BCR) across multifunctional landscapes.
Mixed-species planting promotes forest growth and enhances ecological functions. However, it remains unclear whether mixed forests are more conducive to fostering species diversity than monocultures over long time scales. To address this, we established fixed plots of 140 m × 40 m, 70 m × 50 m, and 70 m × 40 m in plantations of Mytilaria laosensis (Lecomte) and Michelia macclurei (Dandy) in southern Guangxi, China. We analyzed species composition and diversity characteristics using the Jaccard similarity index, principal component analysis and four traditional diversity indices. The results indicated: 1) The species composition similarity between mixed forest and monocultures was higher than that observed between monocultures. 2) As plot area increased, the species richness (SR) and Shannon diversity index (Hʹ) of stands and regenerations followed a power-law distribution, while species abundance increased linearly and evenness (EH) gradually decreased. 3) Regeneration was sparse in the M. laosensis stand but denser in the other two forests. 4) The majority of regenerations had a ground diameter (GD) smaller than 5 cm. Both SR and Hʹ decreased rapidly at first and then more slowly as GD increased, approaching 0, while the relationship between EH and GD was weak. Three out of four species diversity indices of the mixed forest were higher than those of the M. laosensis stand and the mean values of the two monocultures. These results suggest that planting patterns significantly alter the species composition and diversity of plantations, while emphasizing the importance of tree species selection in promoting species diversity.
Abstract Alternative polyadenylation (APA) generates transcript diversity by producing mRNA isoforms with distinct 3’ ends. Despite the critical roles that APA plays in various biological processes, the mechanisms regulating APA in response to stresses have remained poorly understood in plants. Here, we perform comprehensive analysis of APA in tomato, and focus on a phosphate (Pi)- regulated APA gene SlSPX5, encoding a putative Pi sensor protein. SlSPX5 interacts with and sequesters the transcription factor SlPHL1 in the cytosol, thereby inhibiting the expression of Pi starvation inducible genes. We discover that a cis-natural antisense RNA (cis-NAT) is activated from SlSPX5 to promote its proximal polyadenylation under Pi-depleted conditions. The transcription of this cis-NAT induces RNA Polymerase II pausing, generating Ser2 phosphorylation signals that recruit polyadenylation machinery to the 5’ end of SlSPX5. Our findings demonstrate that a cis-NAT regulates APA of its cognate gene in response to Pi starvation.
Cadmium (Cd) is a toxic metal that poses an environmental risk, but its effects on amphibious plants like Hygrophila difformis, which thrive in both terrestrial and submerged conditions, remain unexplored. This study investigates the morphological, anatomical, and physiological responses of H. difformis to Cd exposure. H. difformis was exposed to 0, 1, 2, and 4 mg/L Cd for 30 days under both terrestrial and submerged conditions. Leaves were examined at 10, 20, and 30 days for morphological and physiological changes. At lower Cd concentration (1 mg/L), leaf morphology showed minimal changes compared to the control. Submerged control leaves were highly dissected (26.1 ± 0.45), whereas dissection was substantially reduced at 4 mg/L Cd (16.96 ± 0.67), indicating a profound impact on heterophylly. Cd stress affected leaf size significantly, particularly in submerged plants (17.8 ± 3.22 cm²) compared to controls (39.2 ± 2.84 cm²). Furthermore, compared to terrestrial leaves (4.27 ± 0.31 mg/kg), submerged leaves accumulated significantly higher content of Cd (45.2 ± 6.66 mg/kg), indicating higher absorption under aquatic conditions. Terrestrial leaves appeared more resistant; however, higher concentrations caused tissue damage. Following 30 days of treatment, qualitative TEM-based anatomical analysis revealed noticeable cell shrinkage and fewer visible chloroplasts in submerged leaves compared to controls, while terrestrial leaves exhibited thicker cell walls. Cd exposure also inhibited photosynthesis, reducing pigment levels and enzyme activity. Interestingly, Rubisco activity increased in submerged leaves after 30 days of high Cd exposure, preventing the transition from C3 to C4 photosynthesis. H. difformis exhibits poor growth under Cd stress and can serve as a bioindicator for heavy metal pollution.
BL and UV Ceti are a nearby (2.7 pc) binary system with similar masses, spectral types, and rapid rotation rates, but very different magnetic activity. UV Ceti's much stronger large-scale magnetic field may cause this difference, highlighting key unanswered questions about dynamo processes in fully convective objects. Here we present multi-epoch characterization of the radio spectrum of UV Ceti spanning 1-105 GHz, exhibiting flared emission similar to coronal activity, auroral-like emission analogous to planetary magnetospheres, and slowly-varying persistent emission. Radio observations are a powerful means to probe the role that the large-scale magnetic field of UV Ceti has in non-thermal particle acceleration, because radio-frequency phenomena result from both the activity of small-scale field features as well as large-scale auroral current systems. We find temporal variability at all bands observed, and a hint of rotational modulation in the degree of circular polarization up to 40 GHz. The persistent component of the emission is fairly constant from 1-105 GHz, making optically thick emission or optically thin gyrosynchrotron from electrons with an isotropic pitch angle distribution unlikely. We discuss the possibility of emission mechanisms analogous to Jupiter's radiation belts.
Fabrice Mayran de Chamisso, Loïc Cotten, Valentine Dhers
et al.
With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.
Sugarcane aphid has emerged as a major pest of sorghum recently, and a few sorghum accessions were identified for resistance to this aphid so far. However, the molecular and genetic mechanisms underlying this resistance are still unclear. To understand these mechanisms, transcriptomics was conducted in resistant Tx2783 and susceptible BTx623 sorghum genotypes infested with sugarcane aphids. A principal component analysis revealed differences in the transcriptomic profiles of the two genotypes. The pathway analysis of the differentially expressed genes (DEGs) indicated the upregulation of a set of genes related to signal perception (nucleotide-binding, leucine-rich repeat proteins), signal transduction [mitogen-activated protein kinases signaling, salicylic acid (SA), and jasmonic acid (JA)], and plant defense (transcription factors, flavonoids, and terpenoids). The upregulation of the selected DEGs was verified by real-time quantitative PCR data analysis, performed on the resistant and susceptible genotypes. A phytohormone bioassay experiment showed a decrease in aphid population, plant mortality, and damage in the susceptible genotype when treated with JA and SA. Together, the results indicate that the set of genes, pathways, and defense compounds is involved in host plant resistance to aphids. These findings shed light on the specific role of each DEG, thus advancing our understanding of the genetic and molecular mechanisms of host plant resistance to aphids.
Daniel Kurjak, Peter Petrík, Alena Sliacka Konôpková
et al.
Abstract Key message Seven European beech provenances differing largely in growth performance were grown at two common garden sites in Germany and Slovakia. The intra-specific variability of most traits was explained more by phenotypic plasticity than inter-provenance variability, and efficiency-related traits showed a higher phenotypic plasticity than safety-related traits. Context To maintain climate-resilient future forests, replicated common-garden experiments are suited for developing assisted migration strategies for key tree species. Aims We analysed the magnitude of inter-provenance variability and phenotypic plasticity for 12 functional traits of European beech (Fagus sylvatica L.) and analysed whether the climate at the place of origin left an imprint. Moreover, we asked whether growth is unrelated to xylem safety and to what extent the foliar, xylem and growth-related traits are coordinated. Methods Terminal branches were collected from 19-year-old and 22-year-old trees of seven European beech provenances planted at two common garden sites in Germany and Slovakia, respectively. Three hydraulic, three wood anatomical and four foliar traits were measured and related to two growth-related variables. Results At the two sites, the same pair of provenances showed the highest and lowest growth. Nevertheless, a high degree of phenotypic plasticity was observed, as all traits differed significantly between sites after accounting for provenance effects, with hydraulic safety-related traits showing the lowest and efficiency-related traits the highest plasticity. There was no evidence for inter-provenance variability in xylem embolism resistance (P 50) or the foliar carbon isotope signature (δ13C), a proxy for intrinsic water use efficiency (iWUE), and both were unrelated to growth. P 50 was positively correlated with the lumen-to-sapwood area ratio and vessel density. Conclusions Because of the lacking trade-off between embolism resistance and growth, highly productive provenances can be selected without reducing the drought tolerance of the branch xylem. However, as xylem safety is only one element of a trees’ drought response, it may be beneficial to select provenances with other more conservative drought adaptations such as smaller vessel lumen areas for increasing xylem safety and small supported total leaf areas for reduction of total transpiration.
The eight-toothed spruce bark-beetle Ips typographus is the most damaging insect pest of Norway spruce in Europe, and it poses a serious risk to spruce in other countries where it is not currently present but might be introduced. The beetle is not native to the UK and before 2018 it had not been found established anywhere within the country. In November 2018, however, several adult Ips typographus were found in a billet trap set up as part of annual surveys that the UK carries out to monitor for this and other quarantine bark beetle pests. The finding of adult beetles in the billet trap, a few miles south of Ashford in Kent, led to the discovery of a breeding population in an adjacent woodland. Delimiting surveys to 1 km and further surveys to 50 km showed that the infestation was confined to a single stand of Norway spruce. The stand was felled in January and February 2019, and the material destroyed, and beetles emerging on the site were trapped out using pheromone traps, billet piles and trap trees. These prompt actions eradicated the breeding population, but small numbers of adult Ips typographus continued to be caught on the outbreak site in 2020 and 2021. These captures, and numerous adult Ips typographus caught in pheromone traps set up across the region in response to the outbreak, indicate that incursions of adult Ips typographus are occurring on a regular basis, most likely from source populations in northern France and Belgium. The arrival of adult Ips typographus over a wide area and the potential for further outbreaks represents a continuing threat to spruce woodlands in south-east England, and has important implications for surveillance and monitoring and the management of spruce in this part of the UK.
Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.
Bryce Allen Bagley, Navin Khoshnan, Claudia K Petritsch
As Evolutionary Dynamics moves from the realm of theory into application, algorithms are needed to move beyond simple models. Yet few such methods exist in the literature. Ecological and physiological factors are known to be central to evolution in realistic contexts, but accounting for them generally renders problems intractable to existing methods. We introduce a formulation of evolutionary games which accounts for ecology and physiology by modeling both as computations and use this to analyze the problem of directed evolution via methods from Reinforcement Learning. This combination enables us to develop first-of-their-kind results on the algorithmic problem of learning to control an evolving population of cells. We prove a complexity bound on eco-evolutionary control in situations with limited prior knowledge of cellular physiology or ecology, give the first results on the most general version of the mathematical problem of directed evolution, and establish a new link between AI and biology.
Autonomous agriculture applications (e.g., inspection, phenotyping, plucking fruits) require manipulating the plant foliage to look behind the leaves and the branches. Partial visibility, extreme clutter, thin structures, and unknown geometry and dynamics for plants make such manipulation challenging. We tackle these challenges through data-driven methods. We use self-supervision to train SRPNet, a neural network that predicts what space is revealed on execution of a candidate action on a given plant. We use SRPNet with the cross-entropy method to predict actions that are effective at revealing space beneath plant foliage. Furthermore, as SRPNet does not just predict how much space is revealed but also where it is revealed, we can execute a sequence of actions that incrementally reveal more and more space beneath the plant foliage. We experiment with a synthetic (vines) and a real plant (Dracaena) on a physical test-bed across 5 settings including 2 settings that test generalization to novel plant configurations. Our experiments reveal the effectiveness of our overall method, PPG, over a competitive hand-crafted exploration method, and the effectiveness of SRPNet over a hand-crafted dynamics model and relevant ablations.
Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available Plant Pathology 2021 dataset and can classify multiple diseases in a given plant leaf. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations to aid the farmers manage their yields effectively.
This work examines a set of features that impact the reliability of linear models within the context of plant-wide control design (PWC). The study case is the Tennessee-Eastman (TE) plant. This benchmark problem is well-known for challenging many control design approaches. Analyses involve eigenvalues, average errors between simulations, condition numbers, and loss of rank across frequencies. These studies offer guidance for designing an effective plant-wide control system based on linear models.
This paper briefly describes the device - the phytosensor - for measuring physiological and electrophysiological parameters of plants. This system is developed as a bio-physiological sensor in precise agriculture, as a tool in plant research and environmental biology, and for plant enthusiasts in smart home or entertainment applications. The phytosentor measures main physiological parameters such as the leaf transpiration rate, sap flow, tissue conductivity and frequency response, biopotentials (action potentials and variation potentials), and can conduct electrochemical impedance spectroscopy with organic tissues. Soil moisture and temperature, air quality (CO2, NO2, O3 and other sensors on I2C bus), and general environmental parameters (light, temperature, humidity, air pressure, electromagnetic and magnetic fields) are also recorded in real time. In addition to phytosensing, the device can also perform phytoactuation, i.e. execute electrical or light stimulation of plants, control irrigation and lighting modes, conduct fully autonomous experiments with complex feedback-based and adaptive scenarios in robotic or biohybrid systems. This article represents the revised and extended version of original paper and includes some descriptions and images from the FloraRobotica and BioHybrids projects.
Susana A. Llivisaca-Contreras, Fabián León-Tamariz, Patricia Manzano-Santana
et al.
Mortiño is a member of the Ericaceae family native to the Andes that has been used by local communities for centuries. This species has shown potential in the areas of medicine, agronomy, and green technology. We used a multidisciplinary approach to review aspects related to the ecology, horticulture, composition and potential biotechnological applications of mortiño. As interest in this species grows, care must be taken to identify opportunities that justify its sustainable use while emphasizing the development of local communities. Mapping the wide variety of potential uses and the current state of conservation and utilization of this berry will help researchers to better target mortiño’s potential.
Marcello De Vitis, Marcello De Vitis, Kayri Havens
et al.
The U.N. Decade on Ecosystem Restoration aims to accelerate actions to prevent, halt, and reverse the degradation of ecosystems, and re-establish ecosystem functioning and species diversity. The practice of ecological restoration has made great progress in recent decades, as has recognition of the importance of species diversity to maintaining the long-term stability and functioning of restored ecosystems. Restorations may also focus on specific species to fulfill needed functions, such as supporting dependent wildlife or mitigating extinction risk. Yet even in the most carefully planned and managed restoration, target species may fail to germinate, establish, or persist. To support the successful reintroduction of ecologically and culturally important plant species with an emphasis on temperate grasslands, we developed a tool to diagnose common causes of missing species, focusing on four major categories of filters, or factors: genetic, biotic, abiotic, and planning & land management. Through a review of the scientific literature, we propose a series of diagnostic tests to identify potential causes of failure to restore target species, and treatments that could improve future outcomes. This practical diagnostic tool is meant to strengthen collaboration between restoration practitioners and researchers on diagnosing and treating causes of missing species in order to effectively restore them.
General. Including nature conservation, geographical distribution
The continuous online monitoring of early signs of plant and crop diseases, at their early stages before a potential spread, is of high importance and necessitates multi-disciplinary techniques. Within this study a proposed technique achieves this goal by exploiting laser physics, textural image analysis, and AI for Shot hole disease. In this technique, specific laser light with a wavelength shorter than a sub-cellular component of an inspected plant, produces an interaction within the sub-cellular components and generates laser speckle patterns which can characterize those specific plant cells' features. The generated laser speckle image data then be quantized by texture analysis and classified by Bayesian networks. Such comparative methods manage to detect the differences at sub-cellular scales, such as nuclei modification, cellular shape, or size deformation, etc. for Shot hole disease with high classification accuracy between the healthy and diseased plants. The technique is capable of continuous online observation and monitoring of the plant or crop diseases via a wireless network at low instrumental cost and may replace the costly ground-truth field works