A CNN-based image detector for plant leaf diseases classification

Falaschetti, Laura; Manoni, Lorenzo; Leo, Denis Di; Pau, Danilo; Tomaselli, Venera; Turchetti, Claudio

doi:10.1016/j.ohx.2022.e00363

Cited by 23 publications

(6 citation statements)

References 27 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Falaschetti et al [12] , dddeeee2222 Our model's use of cutting-edge ViT architecture and a large dataset of plant leaf disease images adds to its high accuracy and shows ViT architecture's potential in a variety of computer vision tasks.…”

Section: Comparative Analysismentioning

confidence: 91%

“…The precision of the Nine Layer Deep CNN Model by G. & J. [10] was 96.46%, the CNN-based Detection System by Mishra et al (2020) was 96.24%, and the CNN-based image detector by Falaschetti et al [12] was 96.32%. The high accuracy of our model is due to the ViT architecture's ability to capture complicated patterns in plant leaf disease images.…”

Section: Comparative Analysismentioning

confidence: 98%

See 1 more Smart Citation

Pathogen Detection in Plant Leaves using Deep Neural Networks with Vision Transformer Technique

2023

jecet

View full text Add to dashboard Cite

Better yields can only be achieved by the prompt diagnosis and treatment of crop diseases. Due to its remarkable success in machine vision, Deep Neural-Network (DNN) model with vision transformer has been used in this research to detect and diagnose plant illnesses via their leaves. The approach used in this implementation was trained on plant Village dataset (includes data for 14 plant species, 38 disease classifications, and healthy plant leaves). Parameters including batch size, dropout, and the total number of epochs were used to measure the accuracy of the models. Results from the disease identification accuracy has shown the DNN model with vision transformer obtained an accuracy of 99.67% in plant leaf disease classification, which was significantly higher than the accuracies achieved by the other models in the comparative analysis study. This suggests that the DNN model has a promise for use in the real-time detection of illnesses in agricultural systems.

show abstract

Section: Comparative Analysismentioning

confidence: 91%

Section: Comparative Analysismentioning

confidence: 98%

Pathogen Detection in Plant Leaves using Deep Neural Networks with Vision Transformer Technique

2023

jecet

View full text Add to dashboard Cite

show abstract

“…In this study, a literature review of previous research serves as the foundation for the investigation. CNN-based models have been widely used to detect diseases in various plants, such as tomato [8], [9], apple [10], [11], grape [11], [12], and many more [13]- [16]. In detecting chili disease, the use of Convolutional Neural Networks (CNN) has also been done by previous researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

View full text Add to dashboard Cite

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.

show abstract

“…Our work makes numerous significant contributions to the development of deep learning applications in agriculture. First, as described in [2], we present a CNN architecture made especially to classify the illnesses in pepper, tomato, and potato leaves. This design makes use of data augmentation tech niques, which entail creating variations by artificially manipulating training data (e.g., flipping, rotating).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases

Sarawagi,

Dhiman,

Pagrotra

et al. 2024

Preprint

View full text Add to dashboard Cite

Early disease detection is crucial for maximizingcrop yields and minimizing financial losses in agriculture. Inorder to categorise leaf diseases in pepper, tomato, and potato plants, this study suggests a revolutionary CNN design. The algorithm collects disease-specific features from input photos by using data augmentation techniques to artificially enlarge the training dataset. ReLU-activated convolutional layers gradually capture features at different levels, from low to high, while max pooling layers minimise spatial dimensionality. After thecharacteristics are extracted, a fully-connected layers with a softmax activation function classify them into illness groups. A well chosen dataset containing labelled healthy and sick leaves is used to train and validate the model. Generalizability to untesteddata is ensured via performance evaluation on an independent testing set. The field of deep learning for agricultural applications benefits from this research. This strategy has the ability to rev- olutionise agricultural practices by enabling automated disease identification. This might result in early interventions, better crop health, and eventually sustainable agricultural production.

show abstract

A CNN-based image detector for plant leaf diseases classification

Cited by 23 publications

References 27 publications

Pathogen Detection in Plant Leaves using Deep Neural Networks with Vision Transformer Technique

Pathogen Detection in Plant Leaves using Deep Neural Networks with Vision Transformer Technique

Chili Leaf Health Classification using Xception Pretrained Model

Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases

Contact Info

Product

Resources

About