An automatic classification and early disease detection technique for herbs plant

Sathiya, V.; Josephine, M.S.; Jeyabalaraja, V.

doi:10.1016/j.compeleceng.2022.108026

Cited by 17 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We in our research paper here have tried to implement one of the models i.e., using OpenCV, and tried to apply Image cartoonification using NPR via GAN with K-Means Clustering algorithm. [8]. (see Fig.…”

Section: Deploymentmentioning

confidence: 99%

Generative Adversial Network Approach for Cartoonifying image using CartoonGAN

Roshani Raut

2024

jes

View full text Add to dashboard Cite

This research paper aims to discuss the conversion of images into cartoons using NPR algorithms. The goal of NPR is to create images that appear to have been produced by traditional artistic media such as painting, drawing, or cartoons. In this paper, we discuss the NPR algorithm used for converting images into cartoons, including edge detection, color simplification, and shading techniques also discuss the advantages and limitations of the NPR algorithm for cartoon image conversion. The proposed method consists of three main steps: training a GAN model, generating cartoon images using the trained GAN model, and stylizing the generated cartoon images using K-means clustering algorithm. In the first step, a GAN model is trained on a dataset of real images and corresponding cartoon images. The generator network of the GAN model takes a noise vector as input and generates a cartoon image. The GAN model is trained using adversarial loss, which encourages the generator network to generate cartoon images that are similar to the corresponding real images. In the second step, the trained GAN model is used to generate cartoon versions of real images. The generated cartoon images are then segmented into regions using K-means clustering algorithm. The segmented regions are then stylized using the colors from the corresponding clusters. In the third step, the stylized regions are combined to generate the final cartoon image. The proposed method is evaluated on a dataset of real images and corresponding cartoon images. The results show that the proposed method outperforms the state-of-the-art methods regarding visual quality and quantitative metrics. The visual quality of the generated cartoon images is also evaluated using human perception studies, which shows that the proposed method produces cartoon images that are visually appealing and similar to the corresponding real images.

show abstract

Section: Deploymentmentioning

confidence: 99%

Generative Adversial Network Approach for Cartoonifying image using CartoonGAN

Roshani Raut

2024

jes

View full text Add to dashboard Cite

show abstract

“…Sathiya et al (2022) [13] claimed that the current disease detection methodologies are unsuitable for real-time, field scenarios because they are subject to prediction errors. This may be attributed to the controlled environment set up during the creation of the dataset (image acquisition), which may be different during the usage of the model.…”

Section: Related Workmentioning

confidence: 99%

“…The study proposed using soft computing techniques (CDD-H_HSC) to automatically identify diseases, especially at the earlier stages where most of the existing solutions fail. Just like Badiger et al (2023) [12] and Sathiya et al (2022) [13] also employed segmentation to separate the diseased area from the input plant leaf image at the preprocessing stage using the Multi-Swarm Coyote Optimization (MSCO) technique. To deal with the curse of dimension in model development, the Improved Chan-Vese Snake Optimization (ICVSO) algorithm is employed to minimize the dimensionality of the features for training.…”

Section: Related Workmentioning

confidence: 99%

PlanteSaine: An Artificial Intelligent empowered mobile application for pests and disease management for maize, tomato, and onion farmers in Burkina Faso

Appiah,

Hackman,

Diallo

et al. 2024

Preprint

View full text Add to dashboard Cite

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine's AI system analyzes these images to provide accurate diagnoses. The application's offline functionality ensures accessibility even in remote areas with limited internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture

show abstract

“…One of the limitations is that the data set used in this study is unbalanced, which reduces the improvement in accuracy of the proposed model. The study (20) applied the Inception V3 model to classify and identify diseases in Basil and mint plants. The study did not analyze false positives, which could be critical in detecting potential misclassification of healthy leaves as infected.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

Angayarkanni,

Jayasimman

2023

IJST

View full text Add to dashboard Cite

Objectives : To suggest a suitable image recognition approach for the early recognition of leaf diseases using hybrid features with genetic algorithm and neural network feature selection technique to maximize the accuracy. Methods: Various image processing techniques are utilized to recognize disease in the leaf. In the pre-processing phase, CNN based de-noising is utilized to remove noise from the image. Next, disease part of the leaf is segmented by Pixel wise Classification approach with an optimization technique. Then, the colour and the segmented area of an image is used to remove out texture features. With GLCM and SIFT technique. Then the appropriate features are selected by Genetic Algorithm and Neural Network based feature selection. Extracted features are classified using an Ensemble classifier for recognizing disease in the leaf. For this experiment images are taken from the plant village data set. In this work, the disease caused by Alternaria solani and Pytopthara infestans pathogens is considered for recognizing the disease in the leaf. 1500 leaves are used. From each image, 1654 features are extracted. There is a 70% training and 30% test data split. Classification accuracy, precision and recall measures are considered for evaluating the proposed work's performance. Findings: The proposed work gives 97.7% classification accuracy, 97.3% precision and 97.5% recall measures with various visual feature descriptors and GANN feature selection. Novelty: The in-depth investigation compares the proposed descriptor GANN_SVM and GANN ES detection and classification technique to local and global SVMs. The suggested descriptor outperforms current approaches for diagnosing Alternaria solani and Pytopthara infestans leaves and the method can be applied to all plants infected leaves.

show abstract

An automatic classification and early disease detection technique for herbs plant

Cited by 17 publications

References 19 publications

Generative Adversial Network Approach for Cartoonifying image using CartoonGAN

Generative Adversial Network Approach for Cartoonifying image using CartoonGAN

PlanteSaine: An Artificial Intelligent empowered mobile application for pests and disease management for maize, tomato, and onion farmers in Burkina Faso

Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

Contact Info

Product

Resources

About