Plant Methods最新文献

{"title":"Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision.","authors":"Shan Xu, Jia Shen, Yuzhen Wei, Yu Li, Yong He, Hui Hu, Xuping Feng","doi":"10.1186/s13007-024-01293-1","DOIUrl":"10.1186/s13007-024-01293-1","url":null,"abstract":"<p><p>Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"166"},"PeriodicalIF":4.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput phenotyping in maize and soybean genotypes using vegetation indices and computational intelligence.","authors":"Paulo E Teodoro, Larissa P R Teodoro, Fabio H R Baio, Carlos A Silva Junior, Dthenifer C Santana, Leonardo L Bhering","doi":"10.1186/s13007-024-01294-0","DOIUrl":"10.1186/s13007-024-01294-0","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, providing higher accuracy when predicting agronomic variables using the VIs select by path analysis as input. Future studies should evaluate other plant traits, such as physiological or nutritional ones, as well as different spectral variables from those evaluated here, with a view to contributing to an in-depth understanding about cause-and-effect relationships between plant traits and spectral variables. Such studies could contribute to more specific HTP at the level of traits of interest","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"164"},"PeriodicalIF":4.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods to optimize optical sensing of biotic plant stress - combined effects of hyperspectral imaging at night and spatial binning.","authors":"Christian Nansen, Patrice J Savi, Anil Mantri","doi":"10.1186/s13007-024-01292-2","DOIUrl":"10.1186/s13007-024-01292-2","url":null,"abstract":"<p><p>In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"163"},"PeriodicalIF":4.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142522599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2023: a soil odyssey-HeAted soiL-Monoliths (HAL-Ms) to examine the effect of heat emission from HVDC underground cables on plant growth.","authors":"Ken Uhlig, Jan Rücknagel, Janna Macholdt","doi":"10.1186/s13007-024-01283-3","DOIUrl":"10.1186/s13007-024-01283-3","url":null,"abstract":"<p><strong>Background: </strong>The use of renewable energy for sustainable and climate-neutral electricity production is increasing worldwide. High-voltage direct-current (HVDC) transmission via underground cables helps connect large production sides with consumer regions. In Germany, almost 5,000 km of new power line projects is planned, with an initial start date of 2038 or earlier. During transmission, heat is emitted to the surrounding soil, but the effects of the emitted heat on root growth and yield of the overlying crop plants remain uncertain and must be investigated.</p><p><strong>Results: </strong>For this purpose, we designed and constructed a low-cost large HeAted soiL-Monolith (HAL-M) model for simulating heat flow within soil with a natural composition and density. We could observe root growth, soil temperature and soil water content over an extended period. We performed a field trial-type experiment involving three-part crop rotation in a greenhouse. We showed that under the simulated conditions, heat emission could reduce the yield and root growth depending on the crop type and soil.</p><p><strong>Conclusions: </strong>This experimental design could serve as a low-cost, fast and reliable standard for investigating thermal issues related to various soil compositions and types, precipitation regimes and crop plants affected by similar projects. Beyond our research question, the HAL-M technique could serve as a link between pot and field trials with the advantages of both approaches. This method could enrich many research areas with the aim of controlling natural soil and plant conditions.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"162"},"PeriodicalIF":4.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142505896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cotton whitefly (Bemisia tabaci) detection and counting with a cost-effective deep learning approach on the Raspberry Pi.","authors":"Zhen Feng, Nan Wang, Ying Jin, Haijuan Cao, Xia Huang, Shuhan Wen, Mingquan Ding","doi":"10.1186/s13007-024-01286-0","DOIUrl":"10.1186/s13007-024-01286-0","url":null,"abstract":"<p><strong>Background: </strong>The cotton whitefly (Bemisia tabaci) is a major global pest, causing significant crop damage through viral infestation and feeding. Traditional B. tabaci recognition relies on human eyes, which requires a large amount of work and high labor costs. The pests overlapping generations, high reproductive capacity, small size, and migratory behavior present challenges for the real-time monitoring and early warning systems. This study aims to develop an efficient, high-throughput automated system for detection of the cotton whiteflies. In this work, a novel tool for cotton whitefly fast identification and quantification was developed based on deep learning-based model. This approach enhances the effectiveness of B. tabaci control by facilitating earlier detection of its establishment in cotton, thereby allowing for a quicker implementation of management strategies.</p><p><strong>Results: </strong>We compiled a dataset of 1200 annotated images of whiteflies on cotton leaves, augmented using techniques like flipping and rotation. We modified the YOLO v8s model by replacing the C2f module with the Swin-Transformer and introducing a P2 structure in the Head, achieving a precision of 0.87, mAP₅₀ of 0.92, and F1 score of 0.88 through ablation studies. Additionally, we employed SAHI for image preprocessing and integrated the whitefly detection algorithm on a Raspberry Pi, and developed a GUI-based visual interface. Our preliminary analysis revealed a higher density of whiteflies on cotton leaves in the afternoon and the middle-top, middle, and middle-down plant sections.</p><p><strong>Conclusion: </strong>Utilizing the enhanced YOLO v8s deep learning model, we have achieved precise detection and counting of whiteflies, enabling its application on hardware devices like the Raspberry Pi. This approach is highly suitable for research requiring accurate quantification of cotton whiteflies, including phenotypic analyses. Future work will focus on deploying such equipment in large fields to manage whitefly infestations.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"161"},"PeriodicalIF":4.7,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overexpression of Vitis GRF4-GIF1 improves regeneration efficiency in diploid Fragaria vesca Hawaii 4.","authors":"Esther Rosales Sanchez, R Jordan Price, Federico Marangelli, Kirsty McLeary, Richard J Harrison, Anindya Kundu","doi":"10.1186/s13007-024-01270-8","DOIUrl":"https://doi.org/10.1186/s13007-024-01270-8","url":null,"abstract":"<p><strong>Background: </strong>Plant breeding played a very important role in transforming strawberries from being a niche crop with a small geographical footprint into an economically important crop grown across the planet. But even modern marker assisted breeding takes a considerable amount of time, over multiple plant generations, to produce a plant with desirable traits. As a quicker alternative, plants with desirable traits can be raised through tissue culture by doing precise genetic manipulations. Overexpression of morphogenic regulators previously known for meristem development, the transcription factors Growth-Regulating Factors (GRFs) and the GRF-Interacting Factors (GIFs), provided an efficient strategy for easier regeneration and transformation in multiple crops.</p><p><strong>Results: </strong>We present here a comprehensive protocol for the diploid strawberry Fragaria vesca Hawaii 4 (strawberry) regeneration and transformation under control condition as compared to ectopic expression of different GRF4-GIF1 chimeras from different plant species. We report that ectopic expression of Vitis vinifera VvGRF4-GIF1 provides significantly higher regeneration efficiency during re-transformation over wild-type plants. On the other hand, deregulated expression of miRNA resistant version of VvGRF4-GIF1 or Triticum aestivum (wheat) TaGRF4-GIF1 resulted in abnormalities. Transcriptomic analysis between the different chimeric GRF4-GIF1 lines indicate that differential expression of FvExpansin might be responsible for the observed pleiotropic effects. Similarly, cytokinin dehydrogenase/oxygenase and cytokinin responsive response regulators also showed differential expression indicating GRF4-GIF1 pathway playing important role in controlling cytokinin homeostasis.</p><p><strong>Conclusion: </strong>Our data indicate that ectopic expression of Vitis vinifera VvGRF4-GIF1 chimera can provide significant advantage over wild-type plants during strawberry regeneration without producing any pleiotropic effects seen for the miRNA resistant VvGRF4-GIF1 or TaGRF4-GIF1.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"160"},"PeriodicalIF":4.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors.","authors":"Hermawan Nugroho, Jing Xan Chew, Sivaraman Eswaran, Fei Siang Tay","doi":"10.1186/s13007-024-01280-6","DOIUrl":"https://doi.org/10.1186/s13007-024-01280-6","url":null,"abstract":"<p><p>This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet's lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"159"},"PeriodicalIF":4.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11481777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: A comprehensive review of in planta stable transformation strategies.","authors":"Jérôme Bélanger, Tanya Rose Copley, Valerio Hoyos-Villegas, Jean-Benoit Charron, Louise O'Donoughue","doi":"10.1186/s13007-024-01282-4","DOIUrl":"https://doi.org/10.1186/s13007-024-01282-4","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"158"},"PeriodicalIF":4.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of the fungal pathogen Didymella segeticola in Camellia sinensis using a DNA-based qRT-PCR assay.","authors":"You Zhang, Yiyi Tu, Yijia Chen, Jialu Fang, Fan'anni Chen, Lian Liu, Xiaoman Zhang, Yuchun Wang, Wuyun Lv","doi":"10.1186/s13007-024-01284-2","DOIUrl":"10.1186/s13007-024-01284-2","url":null,"abstract":"<p><p>The fungal pathogen Didymella segeticola causes leaf spot and leaf blight on tea plant (Camellia sinensis), leading to production losses and affecting tea quality and flavor. Accurate detection and quantification of D. segeticola growth in tea plant leaves are crucial for diagnosing disease severity or evaluating host resistance. In this study, we monitored disease progression and D. segeticola development in tea plant leaves inoculated with a GFP-expressing strain. By contrast, a DNA-based qRT-PCR analysis was employed for a more convenient and maneuverable detection of D. segeticola growth in tea leaves. This method was based on the comparison of D. segeticola-specific DNA encoding a Cys2His2-zinc-finger protein (NCBI accession number: OR987684) in relation to tea plant Cs18S rDNA1. Unlike ITS and TUB2 sequences, this specific DNA was only amplified in D. segeticola isolates, not in other tea plant pathogens. This assay is also applicable for detecting D. segeticola during interactions with various tea cultivars. Among the five cultivars tested, 'Zhongcha102' (ZC102) and 'Fuding-dabaicha' (FDDB) were more susceptible to D. segeticola compared with 'Longjing43' (LJ43), 'Zhongcha108' (ZC108), and 'Zhongcha302' (ZC302). Different D. segeticola isolates also exhibited varying levels of aggressiveness towards LJ43. In conclusion, the DNA-based qRT-PCR analysis is highly sensitive, convenient, and effective method for quantifying D. segeticola growth in tea plant. This technique can be used to diagnose the severity of tea leaf spot and blight or to evaluate tea plant resistance to this pathogen.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"157"},"PeriodicalIF":4.7,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142392472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral imaging for pest symptom detection in bell pepper.","authors":"Marvin Krüger, Thomas Zemanek, Dominik Wuttke, Maximilian Dinkel, Albrecht Serfling, Elias Böckmann","doi":"10.1186/s13007-024-01273-5","DOIUrl":"10.1186/s13007-024-01273-5","url":null,"abstract":"<p><strong>Background: </strong>The automation of pest monitoring is highly important for enhancing integrated pest management in practice. In this context, advanced technologies are becoming increasingly explored. Hyperspectral imaging (HSI) is a technique that has been used frequently in recent years in the context of natural science, and the successful detection of several fungal diseases and some pests has been reported. Various automated measures and image analysis methods offer great potential for enhancing monitoring in practice.</p><p><strong>Results: </strong>In this study, the use of hyperspectral imaging over a wide spectrum from 400 to 2500 nm is investigated for noninvasive identification and the distinction of healthy plants and plants infested with Myzus persicae (Sulzer) and Frankliniella occidentalis (Pergande) on bell peppers. Pest infestations were carried out in netted areas, and images of single plants and dissected leaves were used to train the decision algorithm. Additionally, a specially modified spraying robot was converted into an autonomous platform used to carry the hyperspectral imaging system to take images under greenhouse conditions. The algorithm was developed via the XGBoost framework with gradient-boosted trees. Signals from specific wavelengths were found to be associated with the damage patterns of different insects. Under confined conditions, M. persicae and F. occidentalis infestations were distinguished from each other and from the uninfested control for single leaves. Differentiation was still possible when small whole plants were used. However, application under greenhouse conditions did not result in a good fit compared to the results of manual monitoring.</p><p><strong>Conclusion: </strong>Hyperspectral images can be used to distinguish sucking pests on bell peppers on the basis of single leaves and intact potted bell pepper plants under controlled conditions. Wavelength reduction methods offer options for multispectral camera usage in high-grown vegetable greenhouses. The application of automated platforms similar to the one tested in this study could be possible, but for successful pest detection under greenhouse conditions, algorithms should be further developed fully considering real-world conditions.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"156"},"PeriodicalIF":4.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}