Relevant bibliographies by topics / Maize leaf disease

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Maize leaf disease'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Maize leaf disease"

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Jia, Xiao, Dameng Yin, Yali Bai, et al. "Monitoring Maize Leaf Spot Disease Using Multi-Source UAV Imagery." Drones 7, no. 11 (2023): 650. http://dx.doi.org/10.3390/drones7110650.

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Maize leaf spot is a common disease that hampers the photosynthesis of maize by destroying the pigment structure of maize leaves, thus reducing the yield. Traditional disease monitoring is time-consuming and laborious. Therefore, a fast and effective method for maize leaf spot disease monitoring is needed to facilitate the efficient management of maize yield and safety. In this study, we adopted UAV multispectral and thermal remote sensing techniques to monitor two types of maize leaf spot diseases, i.e., southern leaf blight caused by Bipolaris maydis and Curvularia leaf spot caused by Curvul
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Luo, Jing, Shuze Geng, Chunbo Xiu, Dan Song, and Tingting Dong. "A Curvelet-SC Recognition Method for Maize Disease." Journal of Electrical and Computer Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/164547.

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Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to c
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Mohana Priya. C, Et al. "Customized Semantic Segmentation for Enhanced Disease Detection of Maize Leaf Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 31–37. http://dx.doi.org/10.17762/ijritcc.v11i11.9074.

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Maize leaf images are affected by various diseases. Though many image processing techniques are available to identify diseased segment of a diseased maize leaf image proper methodology to segment every chunk in the leaf as disease, shadow, healthy and background using a single methodology is still in search of. So, a single line of attack is availed using Semantic Segmentation for diseased maize Leaf images through which every pixel in an image is equated to a class. Initially multiple classes in the maize leaf images are Labeled and trained. ImagedataStore and PixelLabelDatastore are used to
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Yang, Shixiong, Jingfa Yao, and Guifa Teng. "Corn Leaf Spot Disease Recognition Based on Improved YOLOv8." Agriculture 14, no. 5 (2024): 666. http://dx.doi.org/10.3390/agriculture14050666.

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Leaf spot disease is an extremely common disease in the growth process of maize in Northern China and its degree of harm is quite significant. Therefore, the rapid and accurate identification of maize leaf spot disease is crucial for reducing economic losses in maize. In complex field environments, traditional identification methods are susceptible to subjective interference and cannot quickly and accurately identify leaf spot disease through color or shape features. We present an advanced disease identification method utilizing YOLOv8. This method utilizes actual field images of diseased corn
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Athallah, Dhea Fesa, Thomas Budiman, and Anton Zulkarnain Sianipar. "Optimizer Evaluation for Maize Leaf Disease Using Transfer Learning with MobileNetV3-Small." Journal of Information Systems and Informatics 7, no. 2 (2025): 1939–54. https://doi.org/10.51519/journalisi.v7i2.1144.

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Manual identification of maize leaf disease presents significant challenges, including time- consuming processes, dependence on expert availability, and a high risk of misdiagnosis due to similar symptoms among different diseases. These limitations often lead to delays in disease management, unstable crop yields, and economic losses for farmers. This study aims to address these issues by evaluating the performance of different optimizers in classifying maize leaf disease using transfer learning with the MobileNetV3-Small architecture. A total of 2,850 images of maize leaf disease were used and
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Hailu, Alemayehu, Tajudin Aliyi, and Bayoush Birke. "SCREENING OF MAIZE INBRED LINES UNDER ARTIFICIAL EPIPHYTOTIC CONDITION FOR THEIR REACTION TO TURCICUM LEAF BLIGHT AND COMMON LEAF RUST." EPH - International Journal of Agriculture and Environmental Research 6, no. 2 (2020): 30–38. http://dx.doi.org/10.53555/eijaer.v5i1.57.

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Common leaf rust (Puccinia sorghi Schw) and Turcicum leaf blight (Exserohilum trurcicum) is the major foliar fungal diseases of maize in Ethiopia causing yield losses in the range of 12% to 61% rely up on the genotypes. Screening was done on 178 (106 quality protein maize and 72 normal maize lines )maize inbred lines against Common leaf rust (CLR) and Turcicum leaf blight (TLB) diseases in order to know the reaction of those maize lines for two consecutive years. The experiment was conducted at Ambo plant protection research center (TLB and CLR) and Bako agricultural research center (TLB only)
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JAN, ROOHI. "Identification of resistant donors for turcicum leaf blight disease of maize among cold tolerant temperate germplasm." Annals of Plant and Soil Research 26, no. 2 (2024): 265–72. http://dx.doi.org/10.47815/apsr.2024.10359.

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Turcicum leaf blight disease of maize is the major biotic factor limiting maize production under temperate agro-climatic conditions of Kashmir. Deployment of disease resistant cultivars is the most effective, ecofriendly and cost-efficient disease management option under these conditions where chemical control of maize diseases is not adopted. In the present study, temperate maize germplasm was evaluated against Exherohilum turcicum causing turcicum leaf blight of maize. Among the evaluated maize genotypes some early maturing cold tolerant inbred lines registered high level of resistance (HR)
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Guo, Hongliang, Mingyang Li, Ruizheng Hou, et al. "Sample Expansion and Classification Model of Maize Leaf Diseases Based on the Self-Attention CycleGAN." Sustainability 15, no. 18 (2023): 13420. http://dx.doi.org/10.3390/su151813420.

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In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In additi
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Patel, Ashish, Richa .., and Aditi Sharma. "Maize Plant Leaf Disease Classification Using Supervised Machine Learning Algorithms." Fusion: Practice and Applications 13, no. 2 (2023): 08–21. http://dx.doi.org/10.54216/fpa.130201.

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Maize is an important staple crop all over the world, and its health is very important for food security. It is important for crop management and yield to find diseases that affect maize plants as soon as possible. In this study, we suggest a new way to classify diseases on maize plant leaves by using supervised machine learning algorithms. Our method uses the power of texture analysis with Gray-Level Co-occurrence Matrix (GLCM) and Gabor feature extraction techniques on the Plant-Village dataset, which has images of both healthy and unhealthy maize leaves. This method uses four supervised mac
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Sopialena, Sopialena, Suyadi Suyadi, and Septri Alfian Noor. "Ecosystem Monitoring on Leaves of Leaf Rust Disease of Maize (Zea mays L.)." Caraka Tani: Journal of Sustainable Agriculture 37, no. 1 (2022): 89. http://dx.doi.org/10.20961/carakatani.v37i1.34920.

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Endemic leaf rust disease always occurs in almost all maize plantations in Indonesia. Furthermore, the development of this disease differs concurrently and is greatly influenced by the ecological conditions of maize cultivation. Therefore, this study fills the epidemiological gap of diseases that has not been conducted against the epidemiology of maize rust. This identifies the causes of leaf rust that attacked the maize plants in two locations, namely Bayur and Muang Dalam, Lempake, Samarinda, Indonesia. This study also analyzed the relationship or model between ecological factors of temperat
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Dissertations / Theses on the topic "Maize leaf disease"

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Donahue, Patrick J. "Inheritance of reactions to gray leaf spot and maize dwarf mosaic virus in maize and their associations with physiological traits." Diss., Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/54518.

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Gray leaf spot, caused by Cercospora zeae-maydis, can be a yield-limiting factor in maize where continuous minimum tillage practices are followed. Commercial corn hybrids were evaluated for response to gray leaf spot for seven years at two Virginia locations (Shenandoah and Wythe Counties) and one year at a third location in Virginia (Montgomery County). Yield losses, when comparing resistant to susceptible classes, were approximately 2,000 kg ha⁻¹ at Wythe County in 1982, 750 kg ha⁻¹ at Shenandoah County in 1984, and 2,150 kg ha⁻¹ at Montgomery County in 1988. The inheritance of reaction to g
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Lehmensiek, Anke. "Genetic mapping of gray leaf spot resistance genes in maize." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51776.

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Thesis (PhD)--Stellenbosch University, 2000.<br>ENGLISH ABSTRACT: Gray leaf spot (GLS) of maize, caused by the fungus Cercospora zeae-maydis, can reduce grain yields by up to 60% and it is now recognized as one of the most significant yield-limiting diseases of maize in many parts of the world. The most sustainable and long-term management strategy for GLS will rely heavily on the development of high-yielding, locally adapted GLS resistant hybrids. Molecular markers could be useful to plant breeders to indirectly select for genes affecting GLS resistance and to identify resistance genes
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Du, Min. "A greenhouse screening method for resistance to gray leaf spot in maize." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/42953.

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Christie, Nanette. "Transcriptional regulation underlying the quantitative genetic response of maize to grey leaf spot disease." Thesis, University of Pretoria, 2014. http://hdl.handle.net/2263/79215.

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Cercospora zeina causes grey leaf spot (GLS), a yield-limiting disease on maize. The main objective of this study was to exploit maize gene expression data to dissect the quantitative disease response to C. zeina infection. The project addresses the hypothesis that there is an underlying DNA polymorphism that gives rise to a change in gene expression, which in turn affects GLS disease severity. Genomic and functional annotation of the reporters on an Agilent 44K maize microarray was carried out. This microarray was used for global gene expression profiling of earleaf samples collected from 100
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Ntuli, Jean Felistas. "Characterisation of phytoalexin accumulation in maize inoculated with Cercospora zeina, the causal organism of grey leaf spot disease." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20848.

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Grey Leaf Spot (GLS) is a fungal disease of Zea mays (maize) that is caused by Cercospora zeina. It thrives in sub-tropical climates and causes devastating crop losses of up to 60% in southern Africa where maize is grown as a staple food source. Phytoalexins are low molecular weight anti-microbial bio-chemicals that are synthesised in planta in response to biotic stress. Related studies have characterised many phytoalexins produced in various plants against several diseases. In maize, phytoalexins fall into to two terpenoid groups: kauralexins and zealexins. To date no studies have been carrie
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Gordon, Stuart G. "Genetic Mapping and Components of Resistance to Cercospora Zeae-Maydis in Maize." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1041605948.

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Deng, Yinghai 1966. "Development and disease resistance of leafy reduced stature maize (Zea mays L.)." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38177.

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Previous studies on Leafy reduced-stature (LRS) maize found that it had extremely early maturity and a higher harvest index (HI), leading to high yields for its maturity rating. Whether this apparent high HI is relaxed to its earliness, or can also exist among the medium or late maturity LRS maize has not been previously investigated. It was also of interest to know if the traits that produced the LRS canopy structure have pleiotropic effects on root architecture. Finally, field observations indicated that LRS maize had a lower incidence of common smut. It is not known whether this apparent re
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Dhau, Inos. "Detection, identification, and mapping of maize streak virus and grey leaf spot diseases of maize using different remote sensing techniques." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2866.

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Thesis (PhD. (Geography)) --University of Limpopo, 2019<br>Of late climate change and consequently, the spread of crop diseases has been identified as one of the major threat to crop production and food security in subSaharan Africa. This research, therefore, aims to evaluate the role of in situ hyperspectral and new generation multispectral data in detecting maize crop viral and fungal diseases, that is maize streak virus and grey leaf spot respectively. To accomplish this objective; a comparison of two variable selection techniques (Random Forest’s Forward Variable, (FVS) and Guided Regulari
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Khethisa, Joang Adolf. "A highly accessible application for detection and classification of maize foliar diseases from leaf images." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25359.

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Crop diseases are a major impediment to food security in the developing world. The development of cheap and accurate crop diagnosis software would thus be of great benefit to the farming community. A number of previous studies, utilizing computer vision and machine-learning algorithms, have successfully developed applications that can diagnose crop diseases. However, these studies have primarily focussed either on developing large scale remote sensing applications more suited for large scale farming or on developing desktop/laptop applications and a few others on developing high end smartphone
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Galiano, Carneiro Ana Luísa [Verfasser], and Thomas [Akademischer Betreuer] Miedaner. "Genomics-assisted breeding strategies for quantitative resistances to Northern corn leaf blight in maize (Zea mays L.) and Fusarium diseases in maize and in triticale (× Triticosecale Wittm.) / Ana Luísa Galiano Carneiro ; Betreuer: Thomas Miedaner." Hohenheim : Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim, 2021. http://d-nb.info/123663019X/34.

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Books on the topic "Maize leaf disease"

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Boudreau, Mark Alan. Effects of intercropping beans with maize on angular leaf spot and rust of beans. 1991.

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Book chapters on the topic "Maize leaf disease"

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Sentamilselvan, K., M. Hari Rithanya, T. V. Dharshini, S. M. Akash Nithish Kumar, and R. Aarthi. "Maize Leaf Disease Detection Using Convolutional Neural Network." In Proceedings of Third Doctoral Symposium on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_21.

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Kumar, Bhupendra, Shalini Zanzote Ninoria, and Vibhor Kumar Vishnoi. "Maize disease detection through CNN using leaf images." In Advances in Science, Engineering and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003641544-39.

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Leonard, K. J. "Durable Resistance in the Pathosystems: Maize -Northern and Southern Leaf Blights." In Durability of Disease Resistance. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2004-3_8.

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Walton, J. D. "Molecular Basis of Specificity in Maize Leaf Spot Disease." In Advances in Molecular Genetics of Plant-Microbe Interactions, Vol. 2. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-0651-3_34.

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Singamsetty, Phani Kumar, G. V. N. D. Sai Prasad, N. V. Swamy Naidu, and R. Suresh Kumar. "Maize Leaf Disease Detection and Classification Using Deep Learning." In Artificial Intelligence in Mechanical and Industrial Engineering. CRC Press, 2021. http://dx.doi.org/10.1201/9781003011248-6.

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Zhao, Yu-Xia, Ke-Ru Wang, Zhong-Ying Bai, Shao-Kun Li, Rui-Zhi Xie, and Shi-Ju Gao. "Research of Maize Leaf Disease Identifying Models Based Image Recognition." In Crop Modeling and Decision Support. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01132-0_35.

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Kanakaprabha, S., G. Venu Gopal, N. Saranya, G. Ganesh Kumar, Chittibabulu Sape, and Yallapragada Ravi Raju. "Enhancing Maize Leaf Disease Prediction with Advanced Machine Learning Models." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5081-8_33.

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Panigrahi, Kshyanaprava Panda, Himansu Das, Abhaya Kumar Sahoo, and Suresh Chandra Moharana. "Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_66.

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Kumari, Rajani, and Sandeep Kumar. "Maize Leaf Disease Detection Using Modified Grey Wolf Optimization Technique." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1188-1_34.

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Olayiwola, Joy Oluwabukola, and Jeremiah Ademola Adejoju. "Maize (Corn) Leaf Disease Detection System Using Convolutional Neural Network (CNN)." In Computational Science and Its Applications – ICCSA 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36805-9_21.

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Conference papers on the topic "Maize leaf disease"

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Tijare, P., Pragati D. Bharsakle, Yamini M. Babnekar, Mansi S. Nanwani, Jagruti Sudhakarrao Wankhade, and Sanju Dinesh Garle. "Maize Leaf Disease Detection Using a Transfer Learning Approach." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842875.

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Nasra, Parul, and Sheifali Gupta. "CNN and ResNet50 Performance Comparison for Maize Leaf Disease Detection." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774845.

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Bhuria, Ruchika, and Sheifali Gupta. "PlantPulse: Intelligent CNN-based Analysis for Effective Maize Leaf Disease Detection." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10801138.

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Saha, Dip Kumar, Tushar Deb Nath, Sadman Rafi, and Rounakul Islam Boby. "Classification of Maize leaf disease using improved deep convolutional neural network." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11012983.

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Rai, Harshali M., Mangala P. Shetty, and Spoorthi B. Shetty. "Maize and Peach Leaf Disease Detection Using Image Processing and Machine Learning." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967944.

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Sai, J. Mokshagna, G. Indira Priyadarshini, G. Pranay Goud, D. Haswanth Venkat Sai Varma, R. Pitchai, and D. Jyothirmai. "Smart and sustainable framework for Maize Leaf Disease Prediction using Deep Learning Techniques." In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP). IEEE, 2024. http://dx.doi.org/10.1109/tiacomp64125.2024.00084.

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Kallur, Shanta, Suman Yaligar, and Puneeth N. Thotad. "An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10837823.

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Waema, Damaris, Waweru Mwangi, and Petronilla Muriithi. "A Min-Max Based Data Normalization and Maximum Pooling Approach for Improved Maize Leaf Disease Detection." In 2025 IST-Africa Conference (IST-Africa). IEEE, 2025. https://doi.org/10.23919/ist-africa67297.2025.11060470.

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Sundar, G. Ram, M. V. Nageswara Rao, R. Deepa, S. Selvanayaki, Vaanathi S, and K. Bala Karthik. "Smart IoT-Enabled Deep Learning for Diagnosing Maize Leaf Diseases." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721842.

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Srivastav, Somya, Kalpna Guleria, and Shagun Sharma. "Maizeleafnet Model for Early Identification and Classification of Multi-Class Maize Leaf Diseases." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730847.

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Reports on the topic "Maize leaf disease"

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Harman, Gary E., and Ilan Chet. Enhancement of plant disease resistance and productivity through use of root symbiotic fungi. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7695588.bard.

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The objectives of the project were to (a) compare effects ofT22 and T-203 on growth promotion and induced resistance of maize inbred line Mol7; (b) follow induced resistance of pathogenesis-related proteins through changes in gene expression with a root and foliar pathogen in the presence or absence of T22 or T-203 and (c) to follow changes in the proteome of Mol? over time in roots and leaves in the presence or absence of T22 or T-203. The research built changes in our concepts regarding the effects of Trichoderma on plants; we hypothesized that there would be major changes in the physiology
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Crowley, David E., Dror Minz, and Yitzhak Hadar. Shaping Plant Beneficial Rhizosphere Communities. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7594387.bard.

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PGPR bacteria include taxonomically diverse bacterial species that function for improving plant mineral nutrition, stress tolerance, and disease suppression. A number of PGPR are being developed and commercialized as soil and seed inoculants, but to date, their interactions with resident bacterial populations are still poorly understood, and-almost nothing is known about the effects of soil management practices on their population size and activities. To this end, the original objectives of this research project were: 1) To examine microbial community interactions with plant-growth-promoting r
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Coplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587216.bard.

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Gram-negative plant pathogenic bacteria employ specialized type-III secretion systems (TTSS) to deliver an arsenal of pathogenicity proteins directly into host cells. These secretion systems are encoded by hrp genes (for hypersensitive response and pathogenicity) and the effector proteins by so-called dsp or avr genes. The functions of effectors are to enable bacterial multiplication by damaging host cells and/or by blocking host defenses. We characterized essential hrp gene clusters in the Stewart's Wilt of maize pathogen, Pantoea stewartii subsp. stewartii (Pnss; formerly Erwinia stewartii)
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