PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach | Paper Citation Record

Observation b5b10be2-a8db-4f8e-a2ac-88d930044899 · outbound

This paper cites Solving current lim- itations of deep learning based approaches for plant disease detection.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Solving current lim- itations of deep learning based approaches for plant disease detection

Reference 1

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This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach SciBERT: A Pretrained Language Model for Scientific Text

Reference 2

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This paper cites Weed image augmentation by controlnet-added stable diffusion for multi-class weed detec- tion.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Weed image augmentation by controlnet-added stable diffusion for multi-class weed detec- tion

Reference 3

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This paper cites When synthetic plants get sick: Generating graded plant disease synthetic datasets with novel regression- conditional image-to-image diffusion models (diffusion- pix2pix).

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach When synthetic plants get sick: Generating graded plant disease synthetic datasets with novel regression- conditional image-to-image diffusion models (diffusion- pix2pix)

Reference 4

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This paper cites Multi- temporal unmanned aerial vehicle remote sensing for veg- etable mapping using an attention-based recurrent convolu- tional neural network.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Multi- temporal unmanned aerial vehicle remote sensing for veg- etable mapping using an attention-based recurrent convolu- tional neural network

Reference 5

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Observation a651bdf3-697e-4fdb-a741-0ecf3ee1d5e2 · outbound

This paper cites Deep learning models for plant disease detection and diagnosis.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Deep learning models for plant disease detection and diagnosis

Reference 6

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This paper cites Generative adversarial networks.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Generative adversarial networks

Reference 7

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This paper cites A survey of datasets for computer vision in agriculture.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A survey of datasets for computer vision in agriculture

Reference 8

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This paper cites Training Compute-Optimal Large Language Models.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Training Compute-Optimal Large Language Models

Reference 9

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This paper cites Lora: Low-rank adaptation of large language models.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Lora: Low-rank adaptation of large language models

Reference 10

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Observation 0535a767-6136-4a74-918a-80fe998f30a7 · outbound

This paper cites Grapegan: Unsupervised image enhance- ment for improved grape leaf disease recognition.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Grapegan: Unsupervised image enhance- ment for improved grape leaf disease recognition

Reference 11

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This paper cites Diffusion models in medical imaging: A comprehensive survey.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Diffusion models in medical imaging: A comprehensive survey

Reference 12

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This paper cites Distri- fusion: Distributed parallel inference for high-resolution dif- fusion models.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Distri- fusion: Distributed parallel inference for high-resolution dif- fusion models

Reference 13

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Planning and Rendering: Towards Product Poster Generation with Diffusion Models

Reference 14

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This paper cites A LoRA is Worth a Thousand Pictures.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A LoRA is Worth a Thousand Pictures

Reference 15

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This paper cites A survey of public datasets for computer vision tasks in precision agriculture.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A survey of public datasets for computer vision tasks in precision agriculture

Reference 16

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This paper cites Generative adversarial networks (gans) for image augmentation in agriculture: A systematic review.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Generative adversarial networks (gans) for image augmentation in agriculture: A systematic review

Reference 17

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This paper cites Efficient Estimation of Word Representations in Vector Space.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Efficient Estimation of Word Representations in Vector Space

Reference 18

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This paper cites Analysis of stable diffusion- derived fake weeds performance for training convolutional neural networks.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Analysis of stable diffusion- derived fake weeds performance for training convolutional neural networks

Reference 19

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This paper cites Application of a latent diffusion model to plant disease detection by gen- erating unseen class images.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Application of a latent diffusion model to plant disease detection by gen- erating unseen class images

Reference 20

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This paper cites Harnessing the power of diffusion models for plant disease image augmentation.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Harnessing the power of diffusion models for plant disease image augmentation

Reference 21

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This paper cites General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation

Reference 22

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This paper cites Machine learning and handcrafted image processing methods for classifying common weeds in corn field.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Machine learning and handcrafted image processing methods for classifying common weeds in corn field

Reference 23

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This paper cites Iden- tification of foliar disease regions on corn leaves using slic segmentation and deep learning under uniform background and field conditions.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Iden- tification of foliar disease regions on corn leaves using slic segmentation and deep learning under uniform background and field conditions

Reference 24

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This paper cites Learning transferable visual models from natural language supervi- sion.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Learning transferable visual models from natural language supervi- sion

Reference 25

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Unresolved cited work

Reference 26

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This paper cites Generating of synthetic datasets using diffusion models for solving computer vision tasks in urban applica- tions.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Generating of synthetic datasets using diffusion models for solving computer vision tasks in urban applica- tions

Reference 27

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This paper cites Agribert: Knowledge-infused agricultural language models for match- ing food and nutrition.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Agribert: Knowledge-infused agricultural language models for match- ing food and nutrition

Reference 28

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Explainable artificial intelligence and inter- pretable machine learning for agricultural data analysis

Reference 30

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Photorealistic text-to-image diffusion models with deep language understanding

Reference 31

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Improved techniques for training gans

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This paper cites A systematic review of ex- plainable artificial intelligence models and applications: Re- cent developments and future trends.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A systematic review of ex- plainable artificial intelligence models and applications: Re- cent developments and future trends

Reference 33

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Synthetic image verification in the era of generative artificial intelli- gence: What works and what isn’t there yet

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense

Reference 35

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Dcgan-based data augmentation for tomato leaf disease identification

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This paper cites You don’t have to be perfect to be amazing: Unveil the utility of synthetic images.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach You don’t have to be perfect to be amazing: Unveil the utility of synthetic images

Reference 37

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This paper cites A scoping review on technology applications in agricultural extension.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A scoping review on technology applications in agricultural extension

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PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Unresolved cited work

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This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach The unreasonable effectiveness of deep features as a perceptual metric

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This paper cites A novel few-shot learning framework based on diffusion models for high-accuracy sunflower disease detection and classification.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach A novel few-shot learning framework based on diffusion models for high-accuracy sunflower disease detection and classification

Reference 41

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Observation 1aa9ffed-3e96-4c90-842d-f7b1b89b4998 · outbound

This paper cites Data augmentation using improved cdcgan for plant vigor rat- ing.

PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach Data augmentation using improved cdcgan for plant vigor rat- ing

Reference 42

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