Crop Protection最新文献

{"title":"Optimizing management interventions against Sclerotium rolfsii Sacc. On elephant foot yam (Amorphophallus paeoniifolius (Dennst.) Nicolson) in India","authors":"S.S. Veena ,&nbsp;J. Sreekumar ,&nbsp;M.L. Jeeva ,&nbsp;G. Byju ,&nbsp;G. Suja ,&nbsp;S. Sengupta ,&nbsp;C. Thangamani ,&nbsp;Padmakshi Thakur ,&nbsp;Ashish Narayan ,&nbsp;Pradnya S. Gudadhe ,&nbsp;S. Sunitha","doi":"10.1016/j.cropro.2024.107013","DOIUrl":"10.1016/j.cropro.2024.107013","url":null,"abstract":"<div><div>Collar rot, caused by the fungus <em>Sclerotium rolfsii</em>, is the most widespread and devastating disease affecting elephant foot yam (EFY), leading to significant yield loss. In addition to causing economic damage, high disease incidence results in postharvest rot and a lack of quality planting material for the next season. The increasing incidence of collar rot in the past decade is alarming, and existing management practices have not effectively controlled the pathogen. Therefore, there was an urgent need to develop an effective management strategy to mitigate crop loss. The combination of fungicide, Carbendazim + Mancozeb, bio-agents <em>Trichoderma asperellum</em> and <em>Bacillus amyloliquefaciens</em>, showed high inhibition in lab studies. A preliminary field trial was conducted with these selected bio-agents and fungicide, in addition to the organic amendment vermicompost. Based on the results of the preliminary field trial and another study on managing postharvest rot in elephant foot yam, treatments were finalized, and field trials were conducted over 3 years at ICAR-CTCRI. These results were further validated by testing the same treatments in five states of India. Dipping the corms in a combination fungicide (Carbendazim 12% + Mancozeb 63% WP) for 10 min before storage, treating the corms with cow dung slurry enriched with <em>T. asperellum</em> at 5 g/kg corm three days before planting, and drenching the plant base twice with the same fungicide resulted in the lowest disease incidence (3.19%) and highest yield (36.70 t ha⁻¹) compared to 12.85% disease incidence and 28.37 t ha⁻¹ yield in the control.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107013"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of degree day threshold for Delia platura first emergence in New York State","authors":"Chloe Yi-Luo Cho ,&nbsp;Shea Crowther ,&nbsp;Alexa Stratton ,&nbsp;Dan Olmstead ,&nbsp;Katja Poveda","doi":"10.1016/j.cropro.2024.107011","DOIUrl":"10.1016/j.cropro.2024.107011","url":null,"abstract":"<div><div>Seedcorn maggot <em>(Delia platura)</em> is a globally distributed agricultural pest that feeds on the germinating seeds of economically important crops, including corn and beans. The larvae cause underground damage, which can lead to stand loss. For decades, <em>D. platura</em> has been managed using insecticide-coated seeds, but following the ban on neonicotinoid-coated corn, soy, and wheat seeds in New York State, this practice will no longer be available. Degree day models have been used to predict the emergence of the overwintering generation of agricultural pests since the late 1900s. However, the terminology used in the literature to distinguish degree day thresholds for first emergence and peak emergence is unclear, and previous reports of a 360 degree day emergence threshold did not align with field observations. In 2023, we captured the first emergence at four sites, and in 2024, we monitored adult <em>D. platura</em> at 25 sites in New York State. We observed the first adult emergence between 52 and 197 accumulated degree days (98 ± 7 degree days, mean ± 1 SE) using a biofix of January 1st, confirming that in New York State, <em>D. platura</em> emergence is earlier than previously reported values. Additionally, we note adult activity during December 2023 and January 2024, suggesting that warming winters may impact our ability to predict pest emergence. We propose future models should incorporate both degree day information and other regionally specific factors known to impact pests, including farm management, soil conditions, and landscape composition, for more accurate predictions.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107011"},"PeriodicalIF":2.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Citrus huanglongbing detection: A hyperspectral data-driven model integrating feature band selection with machine learning algorithms","authors":"Kangting Yan ,&nbsp;Xiaobing Song ,&nbsp;Jing Yang ,&nbsp;Junqi Xiao ,&nbsp;Xidan Xu ,&nbsp;Jun Guo ,&nbsp;Hongyun Zhu ,&nbsp;Yubin Lan ,&nbsp;Yali Zhang","doi":"10.1016/j.cropro.2024.107008","DOIUrl":"10.1016/j.cropro.2024.107008","url":null,"abstract":"<div><div>This study explored rapid detection techniques for citrus Huanglongbing (HLB), a disease that severely impacts global citrus production. The method based on hyperspectral technology combined with machine learning algorithms provides new ideas for rapid HLB identification. Algorithm selection is crucial for processing efficiency and hyperspectral data interpretation. Hyperspectral data from healthy, mild HLB-infected, and macular (not related to HLB) citrus leaves were captured using a hyperspectrometer, with qPCR validation. Three preprocessing methods were selected to preprocess the spectral data. Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) were used to extract feature bands from the hyperspectral data, and the range of the number of filtered feature bands as a percentage of the full band was 22.87%–28.31% and 3.27%–4.17%, respectively. Five distinct algorithms were then employed to construct classification models. Upon evaluation, the SPA-STD-SVM algorithm combination proved most effective, boasting a 97.46% accuracy and a 98.55% recall rate. The results demonstrate that suitable machine learning algorithms can effectively classify the hyperspectral data of citrus leaves in three different states: healthy, mild HLB-infected, and macular. This provides an effective approach for using hyperspectral data to differentiate citrus Huanglongbing.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107008"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential biopesticides from Datura alba and Calotropis gigantea: Extraction, analysis, and reported compounds","authors":"Rahim Ullah,&nbsp;Susanne K. Wiedmer","doi":"10.1016/j.cropro.2024.107009","DOIUrl":"10.1016/j.cropro.2024.107009","url":null,"abstract":"<div><div>The growing demand for eco-friendly bio-based agrochemicals with lower health hazards and optimal pest management options boosts the production and utilization of biopesticides. This review provides a comprehensive overview of two toxic weed species <em>Datura alba (Solanaceae</em> family), and <em>Calotropis gigantea (Asclepiadaceae</em> family) with a specific focus on extraction with various solvents, phytochemistry, and biopesticidal activities. The data presented in this article shows that these two toxic weeds species have been studied and reported for biopesticidal activities like antifeedant, ovicidal, insecticidal, larvicidal, antibacterial, and repelling ability against various insects/crop pests. Phytochemical analysis shows that these two weed species have distinct biochemical profiles. However, studies lack systematic screening of the biochemical profiles. In addition, the review highlights the gaps in previous research and suggests that there is a need for well-detailed phytochemical profiling, spectrum of action and mode of action for the future use of these weed plants in the development of biological control agents.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107009"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adding non-crop plants enhances parasitoid fitness on potato plants, but not pest densities","authors":"Yusha Wang ,&nbsp;Yongping Zhou ,&nbsp;Ruwen Li ,&nbsp;Ambreen Masqsood ,&nbsp;Hongsong Chen ,&nbsp;Zhenqiang Qin ,&nbsp;Jingfang Yang ,&nbsp;Jiali Zhang ,&nbsp;Lin Jing ,&nbsp;Huihua Tan ,&nbsp;Zhongshi Zhou","doi":"10.1016/j.cropro.2024.107010","DOIUrl":"10.1016/j.cropro.2024.107010","url":null,"abstract":"<div><div>Conservation biological control (CBC) aims to enhance pest regulation/suppression by intentionally attracting, retaining, and promoting the fitness and effectiveness of natural enemies. Due to carbohydrate sources being highly limited in agroecosystems, providing alternative carbohydrate sources (i.e., nectar sugars, starches) can enhance the survival, fecundity, and effectiveness of natural enemies. Under laboratory conditions, we analyzed five flowering non-crop plant species in habitat management on the longevity, egg load, and reproductive performance of the parasitoid, <em>Aenasirus bambawalei</em> Hayat (Hymenoptera: Encyrtidae), a key natural enemy of mealybug <em>Phenacoccus solenopsis</em> (Hemiptera: Pseudococcidae). Moreover, we measured the influence of the flowering candidates on the host preference and population growth of <em>P. solenopsis</em>. All five flowering non-crop plant species, <em>Fagopyrum esculentum</em> (Polygonaceae), <em>Tagetes erecta</em> (Asteraceae), <em>Vicia faba</em> (Fabaceae), <em>Lobularia maritima</em> (Brassicaceae), and <em>Coriandrum sativum</em> (Apiaceae) promoted the longevity and egg load of <em>A. bambawalei</em> when compared with water only. However, only the females fed on <em>F. esculentum</em>, <em>V. faba,</em> and <em>L. maritima</em> produced 16% and 13% higher total offspring and female offspring than on water, respectively. None of these five non-crop plant species increased the population of mealybugs on crop plants or in the overall system (non-crop plants + crop plants), and there was no significant difference in their abundance across the five different plant combinations. All five flowering candidates positively affected the longevity and/or reproductive capabilities of <em>A. bambawalei</em> without causing an increase in the population of mealybugs on crop plants.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107010"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-sample cucumber disease identification based on multimodal self-supervised learning","authors":"Yiyi Cao ,&nbsp;Guangling Sun ,&nbsp;Yuan Yuan ,&nbsp;Lei Chen","doi":"10.1016/j.cropro.2024.107006","DOIUrl":"10.1016/j.cropro.2024.107006","url":null,"abstract":"<div><div>It is difficult and costly to obtain large-scale, labeled crop disease data in the field of agriculture. How to use small samples of unlabeled data for feature learning has become an urgent problem that needs to be solved. The emergence of self-supervised contrastive learning methods and self-supervised mask learning methods can solve the problem of missing labels on the training data. However, each of these paradigms comes with its own advantages and drawbacks. At the same time, the features learned by dataset in a single modality are limited, ignoring the correlation with other modal information. Hence, this paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes. Integrating image self-supervised mask learning, image self-supervised contrastive learning, and multimodal image-text contrastive learning, the model can not only learn disease feature information from different modalities, but also capture global and local disease feature information. Simultaneously, the mask learning branch was enhanced by introducing a prompt learning module based on a cross-attention network. This module aided in approximately locating the masked regions in the image data in advance, facilitating the decoder in making accurate decoding predictions. Experimental results demonstrate that the proposed method achieves a 95% accuracy in cucumber disease identification in the absence of labels. The approach effectively uncovers high-level semantic features within multimodal small-sample cucumber disease data. GradCAM is also employed for visual analysis to further understand the decision-making process of the model in disease identification. In conclusion, the proposed method in this paper is advantageous for enhancing the classification accuracy of small-sample cucumber data in a multimodal, unlabeled context, demonstrating good generalization performance.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107006"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Labor-saving application of thifluzamide and tricyclazole to seedling trays for integrated control of rice blast and sheath blight","authors":"Xugen Shi ,&nbsp;Kang Qiao ,&nbsp;Yong Zhang ,&nbsp;Shouan Zhang ,&nbsp;Yong Liu ,&nbsp;Xianpeng Zhang ,&nbsp;Baotong Li ,&nbsp;Ruqiang Cui","doi":"10.1016/j.cropro.2024.107004","DOIUrl":"10.1016/j.cropro.2024.107004","url":null,"abstract":"<div><div>Rice blast (<em>Magnaporthe grisea</em>) and sheath blight (<em>Rhizoctonia solani</em>) are limiting factors for rice production. Co-infection of these pathogens results in a disease complex which is difficult to control. In China, growers are accustomed to applying individual fungicides to manage blast and sheath blight, which require large amounts of labor. Application to seedling trays is a new promising solution for saving labor. In this study, application of combined fungicides with different modes of action to seedling trays to integrate control rice blast and sheath blight was evaluated in <em>vitro</em> assays and field trials. The results showed that the combination of thifluzamide and tricyclazole in the 1:2 ratio had significant synergistic inhibitory effects on the mycelial growth of <em>M. grisea</em> and <em>R. solani</em>, with a synergistic ratio (SR) of 2.17 and 1.49. Results from field trials revealed that thifluzamide + tricyclazole at 1107 and 958.5 g/ha applied to seedling trays was the most effective treatment to reduce the disease index of rice blast with control effects of 83.74–84.96% and 81.34–83.26% in 2022 and 2023, respectively, and no significant differences were observed from tricyclazole at 300 g/ha as foliar sprays twice. Compared to the untreated control, disease index of rice sheath blight was notably reduced by all treatments containing thifluzamide. The highest control was recorded in the treatment of thifluzamide + tricyclazole applied at 1107 g/ha to seedling trays. Moreover, compared to the untreated control, all treatments significantly enhanced rice grain yield by 7.67–17.86% and 3.38–18.91% in 2022 and 2023, respectively. The greatest yield (7429.73 and 7404.73 kg/ha in 2022 and 2023, respectively) was observed from the treatment of thifluzamide + tricyclazole at 1107 g/ha applied to seedling trays, with no significant differences among all the treatments containing tricyclazole. Taken together, these results indicated that seedling tray application of thifluzamide + tricyclazole could be a labor-saving approach to the integrated control of rice blast and sheath blight disease complex, while increasing rice grain yield.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"187 ","pages":"Article 107004"},"PeriodicalIF":2.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EQID: Entangled quantum image descriptor an approach for early plant disease detection","authors":"Ishana Attri,&nbsp;Lalit Kumar Awasthi (Prof) ,&nbsp;Teek Parval Sharma","doi":"10.1016/j.cropro.2024.107005","DOIUrl":"10.1016/j.cropro.2024.107005","url":null,"abstract":"<div><div>In present day agriculture, early and accurate identification of plant diseases is essential for prompt response, which protects crop quality and output. This paper presents the Entangled Quantum-Inspired Deep learning model (EQID), a unique method that improves feature representation and classification in plant disease prediction by utilizing the concepts of quantum computing. Two different datasets with images of potatoes and tomatoes as leaves were used to test the EQID model, which performed better than traditional models. EQID obtained 98.96% accuracy, 98.98% precision, 98.96% recall, and 98.90% F1 score on images of potato leaves. For tomato leaves, comparable outcomes were noted, with accuracy, precision, recall, and F1 score all above 99.61%. The accuracy of disease prediction is greatly increased by the efficient and effective feature representation made possible by the EQID model's inclusion of quantum computing techniques. Additionally, the model outperformed other cutting-edge models such as DenseNet-121, VGGNet 16, and Xception Net, illustrating the potentially revolutionary effects of quantum-inspired models in agriculture. Future work will focus on applying the EQID model to a broader range of crops and plant diseases, as well as incorporating additional data sources to further enhance the model's predictive capabilities.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107005"},"PeriodicalIF":2.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-shot agricultural pest recognition based on multimodal masked autoencoder","authors":"Yinshuo Zhang ,&nbsp;Lei Chen ,&nbsp;Yuan Yuan","doi":"10.1016/j.cropro.2024.106993","DOIUrl":"10.1016/j.cropro.2024.106993","url":null,"abstract":"<div><div>Visual recognition methods based on deep convolutional neural networks have performed well in pest diagnosis and have gradually become a research hotspot. However, agricultural pest recognition faces challenges such as few-shot learning, category imbalance, similarity in appearance, and small pest targets. Existing deep learning-based pest recognition methods typically rely solely on unimodal image data, which results in a model whose recognition performance is heavily dependent on the size and quality of the annotated training dataset. However, the construction of large-scale, high-quality pest datasets requires significant economic and technical costs, limiting the practical generalization of existing methods for pest recognition. To address these challenges, this paper proposes a few-shot pest recognition model called MMAE (multimodal masked autoencoder). Firstly, the masked autoencoder of MMAE integrates self-supervised learning, which can be applied to few-shot datasets and improves recognition accuracy. Secondly, MMAE embeds textual modal information on top of image modal information, thus improving the performance of pest recognition by utilizing the correlation and complementarity between the two modalities. The experimental results show that MMAE is the most effective for pest identification compared with the existing excellent models, and the identification accuracy is as high as 98.12%, which is 1.61 percentage points higher than the current state-of-the-art MAE method. The work in this paper shows that the introduction of textual information can assist the visual coder in capturing agricultural pest characterization information at a higher level of granularity, providing a methodological reference for solving the problem of agricultural pest recognition under few-shot conditions.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"187 ","pages":"Article 106993"},"PeriodicalIF":2.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset","authors":"Moisés R. Vallejo Pérez ,&nbsp;Juan J. Cetina Denis ,&nbsp;Mariana A. Chan Ley ,&nbsp;Jesús A. Sosa Herrera ,&nbsp;Juan C. Delgado Ortiz ,&nbsp;Ángel G. Rodríguez Vázquez ,&nbsp;Hugo R. Navarro Contreras","doi":"10.1016/j.cropro.2024.107003","DOIUrl":"10.1016/j.cropro.2024.107003","url":null,"abstract":"<div><div>This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (<em>S. lycopersicum</em>) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by <em>Clavibacter michiganesis</em> subsp. <em>michiganensis</em> (Cmm), and the tomato vein-greening associated with <em>Candidatus</em> Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by <em>Bactericera cockerelli</em> insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"188 ","pages":"Article 107003"},"PeriodicalIF":2.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}