# Plant Disease Management Techniques
**Type:** Topics
**Canonical URL:** https://scholariq.org/topics/plant-disease-management-techniques/
## Facts
| Field | Value |
| --- | --- |
| Description | This cluster of papers explores the advances in vegetable grafting techniques, including the use of grafting to improve tolerance to abiotic stresses, manage soilborne pathogens, enhance fruit quality, and promote disease resistance. It also delves into the mechanisms of rootstock-scion interactions, hormonal signaling, and genetic exchange in grafted plants. Additionally, the cluster discusses alternative strategies for soil disinfestation and the impact of grafting on plant responses to various environmental stressors. |
| Domain | Life Sciences |
| Field | Agricultural and Biological Sciences |
| OpenAlex ID | t12660 |
| Works | 268 |
## Topic papers all
Showing 15 of 268.
- [Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification](https://scholariq.org/papers/deep-neural-networks-based-recognition-of-plant-diseases-by-leaf-image/)
- [Using deep transfer learning for image-based plant disease identification](https://scholariq.org/papers/using-deep-transfer-learning-for-image-based-plant-disease-identification/)
- [Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild](https://scholariq.org/papers/deep-convolutional-neural-networks-for-mobile-capture-device-based-crop-disease/)
- [A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network](https://scholariq.org/papers/a-recognition-method-for-cucumber-diseases-using-leaf-symptom-images-based-on/)
- [Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection](https://scholariq.org/papers/solving-current-limitations-of-deep-learning-based-approaches-for-plant-disease/)
- [Recent advances in image processing techniques for automated leaf pest and disease recognition – A review](https://scholariq.org/papers/recent-advances-in-image-processing-techniques-for-automated-leaf-pest-and/)
- [Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection](https://scholariq.org/papers/detection-and-classification-of-citrus-diseases-in-agriculture-based-on/)
- [Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques](https://scholariq.org/papers/automatic-and-reliable-leaf-disease-detection-using-deep-learning-techniques/)
- [A survey of public datasets for computer vision tasks in precision agriculture](https://scholariq.org/papers/a-survey-of-public-datasets-for-computer-vision-tasks-in-precision-agriculture/)
- [Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture](https://scholariq.org/papers/deep-learning-and-computer-vision-in-plant-disease-detection-a-comprehensive/)
- [Few-Shot Learning approach for plant disease classification using images taken in the field](https://scholariq.org/papers/few-shot-learning-approach-for-plant-disease-classification-using-images-taken/)
- [A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits](https://scholariq.org/papers/a-novel-deep-learning-model-for-detection-of-severity-level-of-the-disease-in/)
- [Tools to kill: Genome of one of the most destructive plant pathogenic fungi Macrophomina phaseolina](https://scholariq.org/papers/tools-to-kill-genome-of-one-of-the-most-destructive-plant-pathogenic-fungi/)
- [End-to-End Deep Learning Model for Corn Leaf Disease Classification](https://scholariq.org/papers/end-to-end-deep-learning-model-for-corn-leaf-disease-classification/)
- [Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers](https://scholariq.org/papers/plant-disease-classification-a-comparative-evaluation-of-convolutional-neural/)
## Topic primary papers
- [Tools to kill: Genome of one of the most destructive plant pathogenic fungi Macrophomina phaseolina](https://scholariq.org/papers/tools-to-kill-genome-of-one-of-the-most-destructive-plant-pathogenic-fungi/)
- [Morphological and molecular characterization of Macrophomina phaseolina isolated from three legume crops and evaluation of mungbean genotypes for resistance to dry root rot](https://scholariq.org/papers/morphological-and-molecular-characterization-of-macrophomina-phaseolina-isolated/)
- [Dynamics of Verticillium Species Microsclerotia in Field Soils in Response to Fumigation, Cropping Patterns, and Flooding](https://scholariq.org/papers/dynamics-of-i-verticillium-i-species-microsclerotia-in-field-soils-in-response/)
- [Inhibitory of grey mold on green pepper and winter jujube by chlorine dioxide (ClO2) fumigation and its mechanisms](https://scholariq.org/papers/inhibitory-of-grey-mold-on-green-pepper-and-winter-jujube-by-chlorine-dioxide/)
- [Thrips Mortality and Strawberry Quality after Vacuum Fumigation with Acetaldehyde or Ethyl Formate1,2](https://scholariq.org/papers/thrips-mortality-and-strawberry-quality-after-vacuum-fumigation-with/)
- [Acetaldehyde Fumigation at Reduced Pressures to Control the Green Peach Aphid on Wrapped and Packed Head Lettuce13](https://scholariq.org/papers/acetaldehyde-fumigation-at-reduced-pressures-to-control-the-green-peach-aphid-on/)
- [Enhancing Agricultural Sustainability with Deep Learning: A Case Study of Cauliflower Disease Classification](https://scholariq.org/papers/enhancing-agricultural-sustainability-with-deep-learning-a-case-study-of/)
- [Rootstock increases the physiological defence of tomato plants againstPseudomonas syringaepv.tomatoinfection](https://scholariq.org/papers/rootstock-increases-the-physiological-defence-of-tomato-plants-against-i/)
- [Resistance toFusarium oxysporumf. sp.luffaeinLuffaGermplasm Despite Hypocotyl Colonization](https://scholariq.org/papers/resistance-to-i-fusarium-oxysporum-i-f-sp-i-luffae-i-in-i-luffa-i-germplasm/)
- [Enhancing Crop Health, A Novel CNN-SVM Hybrid Model for Litchi Disease Detection](https://scholariq.org/papers/enhancing-crop-health-a-novel-cnn-svm-hybrid-model-for-litchi-disease-detection/)
- [Few-Shot Learning for Multiclass Disease Detection in Azadirachta Indica](https://scholariq.org/papers/few-shot-learning-for-multiclass-disease-detection-in-azadirachta-indica/)
- [Recognition of Strawberry Powdery Mildew in Complex Backgrounds: A Comparative Study of Deep Learning Models](https://scholariq.org/papers/recognition-of-strawberry-powdery-mildew-in-complex-backgrounds-a-comparative/)
- [Automatic Early Detection of Potato Blight Disease Using Deep Neural Networks](https://scholariq.org/papers/automatic-early-detection-of-potato-blight-disease-using-deep-neural-networks/)
- [MobileNetV3Small-CM FusionNet: A Lightweight Deep Learning Framework for Multi-Class Arecanut Disease Classification Using Feature Fusion](https://scholariq.org/papers/mobilenetv3small-cm-fusionnet-a-lightweight-deep-learning-framework-for-multi/)
- [FINITE ELEMENT SIMULATION OF ELECTROACTIVE MECHANICAL RESPONSE OF SCLERA USING A MULTI-PHYISICS CHEMO-ELECTRO-MECHANICAL MODEL](https://scholariq.org/papers/finite-element-simulation-of-electroactive-mechanical-response-of-sclera-using-a/)
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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.