Works matching Plant diseases
Results: 5000
Derin Transfer Öğrenmeye Dayalı Pirinç Bitkisi Hastalıklarının Tespiti.
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- Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi, 2023, v. 9, n. 4, p. 92, doi. 10.30855/gmbd.0705S10
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CURRENT ISSUES IN PLANT DISEASE CONTROL: BIOTECHNOLOGY AND PLANT DISEASE.
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- Bayero Journal of Pure & Applied Sciences, 2013, v. 6, n. 2, p. 121, doi. 10.4314/bajopas.v6i2.26
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The role of the rhizobiome recruited by root exudates in plant disease resistance: current status and future directions.
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- Environmental Microbiome, 2024, v. 19, n. 1, p. 1, doi. 10.1186/s40793-024-00638-6
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Plant Disease Detection and Classification using Image Processing: A Review.
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- International Journal of Recent Research Aspects, 2017, v. 4, n. 3, p. 22
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Knowledge distillation in plant disease recognition.
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- Neural Computing & Applications, 2022, v. 34, n. 17, p. 14287, doi. 10.1007/s00521-021-06882-y
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Plant disease leaf image segmentation based on superpixel clustering and EM algorithm.
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- Neural Computing & Applications, 2019, v. 31, n. 2, p. 1225, doi. 10.1007/s00521-017-3067-8
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Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval.
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- Plant Methods, 2023, v. 19, n. 1, p. 1, doi. 10.1186/s13007-023-01070-6
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Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition.
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- Frontiers in Plant Science, 2022, v. 13, p. 1, doi. 10.3389/fpls.2022.907916
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Corrigendum: Active substances of myxobacteria against plant diseases and their action mechanisms.
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- Frontiers in Microbiology, 2024, p. 01, doi. 10.3389/fmicb.2024.1392109
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- Article
Fitopatogenne gatunki grzybów i organizmy grzybopodobne zidentyfikowane w próbach roślin dostarczonych do Kliniki Chorób Roślin w latach 2018-2020.
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- Progress in Plant Protection, 2021, v. 61, n. 3, p. 177, doi. 10.14199/ppp-2021-019
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A history of strategies and a tapestry of triumphant tales in tackling plant fungal diseases.
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- Plant Pathology, 2024, v. 73, n. 7, p. 1619, doi. 10.1111/ppa.13950
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Trichoderma and its role in biological control of plant fungal and nematode disease.
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- Frontiers in Microbiology, 2023, p. 01, doi. 10.3389/fmicb.2023.1160551
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- Article
Advances in Irradiation Technology for Plant Disease Resistance: A Review.
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- Agricultural Reviews, 2024, v. 45, n. 1, p. 25, doi. 10.18805/ag.RF-284
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- Article
Plant disease and pest detection using deep learning-based features.
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- Turkish Journal of Electrical Engineering & Computer Sciences, 2019, v. 27, n. 3, p. 1636, doi. 10.3906/elk-1809-181
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Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears.
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- Agriculture; Basel, 2022, v. 12, n. 2, p. 300, doi. 10.3390/agriculture12020300
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An IoT-Enabled Augmented Reality Framework for Plant Disease Detection.
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- Revue d'Intelligence Artificielle, 2021, v. 35, n. 3, p. 185, doi. 10.18280/ria.350301
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- Article
Effective and Sustained Control of Soil-Borne Plant Diseases by Biodegradable Polyhydroxybutyrate Mulch Films Embedded with Fungicide of Prothioconazole.
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- Molecules, 2021, v. 26, n. 3, p. 762, doi. 10.3390/molecules26030762
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Ultralong‐lasting plasma‐activated water inhibits pathogens and improves plant disease resistance in soft rot‐infected hydroponic lettuce.
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- Plasma Processes & Polymers, 2024, v. 21, n. 8, p. 1, doi. 10.1002/ppap.202400039
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Microbial Effectors: Key Determinants in Plant Health and Disease.
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- Microorganisms, 2022, v. 10, n. 10, p. 1980, doi. 10.3390/microorganisms10101980
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- Article
Plant Disease Recognition Model Based on Improved YOLOv5.
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- Agronomy, 2022, v. 12, n. 2, p. 365, doi. 10.3390/agronomy12020365
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PLANT DISEASE RECOGNITION AND CLUSTERING USING FUZZY ALGORITHM ON DATA MINING.
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- ICTACT Journal on Soft Computing, 2021, v. 11, n. 4, p. 2429, doi. 10.21917/ijsc.2021.0346
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A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.
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- Frontiers in Plant Science, 2024, p. 1, doi. 10.3389/fpls.2023.1321877
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A NOVEL APPROACH USING MACHINE LEARNING TO DETECT AND CLASSIFY RICE PLANT DISEASES.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2024, v. 2, n. 1, p. 50, doi. 10.26634/jaim.2.1.20288
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The Impact of Fungal Diseases on Plants in Ecosystems and Ecological Control Methods.
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- Nature & Science / Təbiət və Elm, 2025, v. 7, n. 3, p. 26, doi. 10.36719/2707-1146/54/26-29
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Primjena korisnih mikroorganizama u ekološkoj zaštiti bilja od biljnih bolesti.
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- Glasnik Zastite Bilja, 2023, v. 46, n. 3, p. 40, doi. 10.31727/gzb.46.3.5
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Corn Care: Plant Disease Defender.
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- Grenze International Journal of Engineering & Technology (GIJET), 2024, v. 10, n. 2,Part 4, p. 4643
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Comparative Study using Deep Learning for Detecting Plant Type and Leaf Disease.
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- Grenze International Journal of Engineering & Technology (GIJET), 2024, v. 10, n. 1, Part 3, p. 2924
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Rationalising risk: grower strategies to manage plant disease in the UK wheat and potato sectors.
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- Geographical Journal, 2012, v. 178, n. 4, p. 338, doi. 10.1111/j.1475-4959.2012.00485.x
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Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection.
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- Engineering, Technology & Applied Science Research, 2025, v. 15, n. 1, p. 19947, doi. 10.48084/etasr.8944
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Comparative Analysis of YOLOv8 and YOLOv9 Models for Real-Time Plant Disease Detection in Hydroponics.
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- Engineering, Technology & Applied Science Research, 2024, v. 14, n. 5, p. 17269, doi. 10.48084/etasr.8301
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Coordination as Management Response to the Spread of a Global Plant Disease: A Case Study in a Major Philippine Banana Production Area.
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- Frontiers in Plant Science, 2019, p. 1, doi. 10.3389/fpls.2019.01048
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Edge effects, not connectivity, determine the incidence and development of a foliar fungal plant disease.
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- Ecology, 2011, v. 92, n. 8, p. 1551, doi. 10.1890/10-1072.1
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Computer Vision for Plant Disease Recognition: A Comprehensive Review.
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- Botanical Review, 2024, v. 90, n. 3, p. 251, doi. 10.1007/s12229-024-09299-z
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AN ATTENTION-BASED DEEP NETWORK FOR PLANT DISEASE CLASSIFICATION.
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- Machine Graphics & Vision, 2024, v. 33, n. 1, p. 47, doi. 10.22630/MGV.2024.33.1.3
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Enhanced Fuzzy Deep Learning for Plant Disease Detection to Boost the Agricultural Economic Growth.
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- International Journal of Advanced Computer Science & Applications, 2025, v. 16, n. 2, p. 1352, doi. 10.14569/ijacsa.2025.01602133
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CCNet: CNN CapsNet-Based Hybrid Deep Learning Model for Diagnosing Plant Diseases Using Thermal Images.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 11, p. 721, doi. 10.14569/ijacsa.2024.0151169
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Automatic Plant Disease Detection System Using Advanced Convolutional Neural Network-Based Algorithm.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 8, p. 631, doi. 10.14569/ijacsa.2024.0150863
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A novel plant leaf disease detection by adaptive fuzzy C-Means clustering with deep neural network.
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- Journal of Experimental & Theoretical Artificial Intelligence, 2024, v. 36, n. 5, p. 785, doi. 10.1080/0952813X.2022.2108146
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Plant Disease Detection using CNN Models.
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- International Journal of Next-Generation Computing, 2023, v. 14, n. 1, p. 112
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Plant disease mapping in paddy growing stages using remotely sensed data.
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- Environmental Earth Sciences, 2025, v. 84, n. 1, p. 1, doi. 10.1007/s12665-024-11991-7
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Plants Disease Image Classification Based on Lightweight Convolution Neural Networks.
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- International Journal of Pattern Recognition & Artificial Intelligence, 2022, v. 36, n. 13, p. 1, doi. 10.1142/S0218001422540131
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- Article
REGULARIZED ISOTONIC REGRESSION BASED JENSEN-SHANNON BOOSTING CLASSIFICATION FOR PLANT LEAF DISEASE IDENTIFICATION.
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- Suranaree Journal of Science & Technology, 2022, v. 29, n. 5, p. 1
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IDENTIFYING CROP DISTRESS AND STRESS-INDUCED PLANT DISEASES USING HYPER SPECTRAL IMAGE ANALYSIS.
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- Scalable Computing: Practice & Experience, 2024, v. 25, n. 6, p. 4518, doi. 10.12694/scpe.v25i6.3258
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Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features.
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- Agricultural Engineering International: CIGR Journal, 2013, v. 15, n. 1, p. 211
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Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification.
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- International Journal of Remote Sensing, 2024, v. 45, n. 5, p. 1608, doi. 10.1080/01431161.2024.2313995
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Oxathiapiprolin, a Novel Chemical Inducer Activates the Plant Disease Resistance.
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- International Journal of Molecular Sciences, 2020, v. 21, n. 4, p. 1223, doi. 10.3390/ijms21041223
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RDA-CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification.
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- Computer Systems Science & Engineering, 2022, v. 42, n. 1, p. 33, doi. 10.32604/csse.2022.022206
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Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection.
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- Artificial Intelligence Review, 2020, v. 53, n. 7, p. 4993, doi. 10.1007/s10462-020-09813-w
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A Review on Advances in Automated Plant Disease Detection.
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- International Journal of Engineering & Technology Innovation, 2021, v. 11, n. 4, p. 251, doi. 10.46604/ijeti.2021.8244
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Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey.
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- EAI Endorsed Transactions on Internet of Things, 2024, v. 10, p. 1, doi. 10.4108/eetiot.4595
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