Cadmium (Cd) contamination in agricultural soils poses a serious threat to food security and human health. Nitric oxide (NO), as redox-related signaling molecule, is known to promote plant growth and regulate soil quality in heavy metal-contamination soils. However, the regulatory mechanisms of NO in plant physiology and soil biochemistry have not been well-demonstrated. In this study, we investigated the role of exogenous application of sodium nitroprusside (SNP) as an NO donor additive on the growth performances, Cd accumulation and translocation, physiological biochemical response of plant, soil physicochemical properties, and soil microbial communities of hyperaccumulator Solanum nigrum L. in Cd-contaminated soil. Our results showed that 100 and 200 μmol·L−1 NO addition markedly increased the plant biomass by 16.22 % and 14.85 %, and enhanced the Cd accumulation by 46.91 % and 22.08 % in S. nigrum compared to the 100 mg·kg−1 Cd treatment alone, respectively. Moreover, NO supply could mitigate Cd phytotoxicity and oxidative damage by significantly increasing the activities of antioxidant enzymes and osmoregulatory substances content. In addition, NO addition significantly changes the soil physicochemical properties, including changed the SOC, CEC, the NH4+-N and NO3−-N contents, increased the content of soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and soil enzymatic activities, such as the 100 μmol·L−1 NO treatment increased 4.71 %, 7.45 %, 18.44 % and 29.46 % of the soil pH, EC, the content of NO3−-N and NH4+-N as compared to Cd stress alone under 50 mg·kg−1 Cd concentrations, respectively. Meanwhile, in Cd alone treatment, the soil bacterial diversity indexes were slightly increased, while the fungal diversity slightly decreased at low Cd concentrations and increased at high Cd level compared with no Cd addition groups. After NO addition, the soil bacterial and fungal diversity was enhanced compared to without NO addition. Exogenous NO treatment also significantly changed the structures of soil bacterial and fungal communities, and increased the relative abundance of soil beneficial microbial communities. Furthermore, interactions among soil environmental factors and NO addition significantly influenced dominant bacterial, and fungal taxa. These results provide proof that soil remediation with exogenous NO addition may be an effective method to improve soil microenvironment and enhance plant tolerance to metal stress.
The LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2015 evaluation was actually conducted on a set of more than 100K images illustrating 1000 plant species living in West Europe. The main originality of this dataset is that it was built through a large-scale participatory sensing plateform initiated in 2011 and which now involves tens of thousands of contributors. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
Robin-Nico Kampa, Fabian Deuser, Konrad Habel
et al.
Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.
Zane K J Hartley, Lewis A G Stuart, Andrew P French
et al.
Recent years have seen substantial improvements in the ability to generate synthetic 3D objects using AI. However, generating complex 3D objects, such as plants, remains a considerable challenge. Current generative 3D models struggle with plant generation compared to general objects, limiting their usability in plant analysis tools, which require fine detail and accurate geometry. We introduce PlantDreamer, a novel approach to 3D synthetic plant generation, which can achieve greater levels of realism for complex plant geometry and textures than available text-to-3D models. To achieve this, our new generation pipeline leverages a depth ControlNet, fine-tuned Low-Rank Adaptation and an adaptable Gaussian culling algorithm, which directly improve textural realism and geometric integrity of generated 3D plant models. Additionally, PlantDreamer enables both purely synthetic plant generation, by leveraging L-System-generated meshes, and the enhancement of real-world plant point clouds by converting them into 3D Gaussian Splats. We evaluate our approach by comparing its outputs with state-of-the-art text-to-3D models, demonstrating that PlantDreamer outperforms existing methods in producing high-fidelity synthetic plants. Our results indicate that our approach not only advances synthetic plant generation, but also facilitates the upgrading of legacy point cloud datasets, making it a valuable tool for 3D phenotyping applications.
Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant. In addition to the plant's branching structure, this representation contains parametric information for each plant organ, and can therefore be used directly in a variety of tasks. In this data-driven approach, we train a recursive neural network with virtual plants generated using a procedural model. After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud. Our method is applicable to any plant that can be represented as binary axial tree. We quantitatively evaluate our approach on Chenopodium Album plants on reconstruction, segmentation and skeletonization, which are important problems in plant phenotyping. In addition to carrying out several tasks at once, our method achieves results on-par with strong baselines for each task. We apply our method, trained exclusively on synthetic data, to 3D scans and show that it generalizes well.
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Biological nitrogen (N) fixation is an ecological method used to provide nutrition for crops and reduce fertilizer application in terrestrial ecosystems. Silver nanoparticles (AgNPs) are becoming environmental contaminants, and, thus, could negatively affect the activity and diversity of soil diazotrophs. To test this, a greenhouse pot experiment for growing maize was performed under different concentrations of AgNPs (0, 1, 5, 10, 20 mg kg<sup>−1</sup>). We measured the N<sub>2</sub>-fixation activity and abundance of <i>nifH</i> gene encoding the nitrogenase reductase subunit and analyzed the diversity, composition and co-occurrence networks of diazotrophic communities in maize rhizospheric soil. Results showed that a lower dose of AgNPs did not show significant influence on soil diazotrophs, while a higher dose of AgNPs decreased both soil N<sub>2</sub>-fixation activity and <i>nifH</i> gene abundance, though diazotrophic diversity remained unchanged. AgNPs at 10 mg kg<sup>−1</sup> and 20 mg kg<sup>−1</sup> strongly shifted the community composition of diazotrophs, increasing the proportions of <i>Bradyrhizobium</i> and <i>Paenibacillus,</i> while decreasing <i>Azospirillum</i> and <i>Rhizobium.</i> Network analysis revealed weakened negative associations among species under AgNPs, with keystone taxa shifting from <i>Bradyrhizobium</i>, <i>Geobacter</i>, <i>Azospirillum</i> and <i>Burkholderia</i> to <i>Bradyrhizobium</i>, <i>Paenibacillus</i> and <i>Skermanella</i> under AgNPs. Soil-soluble Ag, dissolved organic carbon and soil pH were identified as the factors most closely driving the diazotrophic community composition. In conclusion, higher doses of AgNPs could inhibit N<sub>2</sub>-fixation activity and shape the diazotrophic communities. These findings provide empirical evidence of AgNPs’ ecological impacts on soil microbial functions.
Predicting whether alien species will invade a native community is a key challenge in invasion ecology. One factor that may help predict invasion success is phylogenetic relatedness. Darwin proposed that closely related species tend to share similar niches, although this relationship may be influenced by various ecological and evolutionary factors. To test this, we classified alien Asteraceae species in China into three categories based on their invasion status and the extent of ecological damage: introduced, naturalized, and invasive. We then compared the genetic relationships and niche overlap between alien and native Asteraceae species. We found that invasive Asteraceae species are more closely related to native Asteraceae species than are introduced and naturalized species. However, alien Asteraceae species (including introduced, naturalized, and invasive species) exhibited relatively low niche overlap with native Asteraceae species. These findings suggest that the main premise underlying Darwin’s naturalization conundrum, namely, the universality of phylogenetic niche conservatism, may not hold true. Instead, our findings indicate that alien species are more likely to invade successfully when they are more closely related to native plants, exhibit less niche overlap, and maintain conservative niches during the invasion process. These findings provide new insights into the mechanisms of alien plant invasions, highlight the relationship between alien species invasions and native community vulnerability, and offer important insights into the development of effective biological invasion management strategies.
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.
Otacilio B. L. Neto, Michela Mulas, Francesco Corona
This work presents a general framework for the advanced control of a common class of activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors and actuators, we design and configure a highly customisable Output Model-Predictive Controller (Output MPC) for the flexible operation of ASPs as water resource recovery facilities. The controller consists of a i) Moving-Horizon Estimator for determining the state of the process, from plant measurements, and ii) a Model-Predictive Controller for determining the optimal actions to attain high-level operational goals. The Output MPC can be configured to satisfy the technological limits of the plant equipment, as well as operational desiderata defined by plant personnel. We consider exemplary problems and show that the framework is able to control ASPs for tasks of practical relevance, ranging from wastewater treatment subject to normative limits, to the production of an effluent with varying nitrogen content, and energy recovery.
Large Language Models (LLMs) have exhibited remarkable capabilities in understanding and interacting with natural language across various sectors. However, their effectiveness is limited in specialized areas requiring high accuracy, such as plant science, due to a lack of specific expertise in these fields. This paper introduces PLLaMa, an open-source language model that evolved from LLaMa-2. It's enhanced with a comprehensive database, comprising more than 1.5 million scholarly articles in plant science. This development significantly enriches PLLaMa with extensive knowledge and proficiency in plant and agricultural sciences. Our initial tests, involving specific datasets related to plants and agriculture, show that PLLaMa substantially improves its understanding of plant science-related topics. Moreover, we have formed an international panel of professionals, including plant scientists, agricultural engineers, and plant breeders. This team plays a crucial role in verifying the accuracy of PLLaMa's responses to various academic inquiries, ensuring its effective and reliable application in the field. To support further research and development, we have made the model's checkpoints and source codes accessible to the scientific community. These resources are available for download at \url{https://github.com/Xianjun-Yang/PLLaMa}.
Abstract Legume-rhizobia root-nodule symbioses involve the recognition of rhizobial Nod factor (NF) signals by NF receptors, triggering both nodule organogenesis and rhizobial infection. RinRK1 is induced by NF signaling and is essential for infection thread (IT) formation in Lotus japonicus. However, the precise mechanism underlying this process remains unknown. Here, we show that RinRK1 interacts with the extracellular domains of NF receptors (NFR1 and NFR5) to promote their accumulation at root hair tips in response to rhizobia or NFs. Furthermore, Flotillin 1 (Flot1), a nanodomain-organizing protein, associates with the kinase domains of NFR1, NFR5 and RinRK1. RinRK1 promotes the interactions between Flot1 and NF receptors and both RinRK1 and Flot1 are necessary for the accumulation of NF receptors at root hair tips upon NF stimulation. Our study shows that RinRK1 and Flot1 play a crucial role in NF receptor complex assembly within localized plasma membrane signaling centers to promote symbiotic infection.
Rebecca Njeri Damaris, Fengxue Tang, Xiaorong Fan
et al.
Plants experiencing salt stress often exhibit an ionic homeostasis disorder as a primary symptom. Transcription factor OsMYBc positively regulates OsHKT1;1 to control Na+ accumulation in rice shoots. In this study, root tissues from a T-DNA insert mutant of osmybcc-1 (also known as OsMYBc) and its wild type were analyzed using proteomic methods after being exposed to salt stress for 12 h. Physiological results showed that the wildtype accumulated and excluded more K+ in the leaf tips. Proteomics results revealed 8523 proteins were identified and 7598 were quantifiable. Differentially expressed proteins (DEPs) that were enriched in DNA repair and recombinase pathways were found when comparing mutants with wild types without salt treatment. However, DEPs involved in response to reactive nitrogen species were enriched in the mutant after salt treatment. Moreover, DEPs in response to reactive nitrogen species such as high-affinity nitrate transporter family member (NRT) were enriched when comparing mutants with wild types and with salt treatment as well. Additionally, the ion and anion transmembrane transporters were downregulated and enriched in both the mutant and wildtype in response to salt stress. The RNA abundance changes observed in genes related to reactive nitrogen species response and ion and anion transmembrane transport were consistent with the proteomic results. The NRT2.1-overexpressing rice was more tolerant to salt stress than its gene knockout mutant nrt2.1. These findings highlighted protein dynamics in roots in response to salt stress, as well as the providing of some potential targets for MYB transcription factor in salt stress mitigation.
The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.