An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset

Aminuddin, Nuramin Fitri; Tukiran, Zarina; Joret, Ariffuddin; Tomari, Razali; Morsin, Marlia

doi:10.14569/ijacsa.2022.0130750

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed that the trained models were effective, with an average accuracy performance of 97% ( Table 7 ). This research demonstrates the significance of data augmentation in improving the accuracy of DL models for assessing chilli health, which could increase agricultural output ( Aminuddin et al., 2022 ).…”

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 82%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

show abstract

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 82%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…In order to train neural networks, these edited photographs are added to the original collection of photos, increasing the data set size. Data augmentation is used to artificially expand the training data set [28].…”

Section: B Data Augmentationmentioning

confidence: 99%

COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function

Mohamed¹,

Elsamahy²,

Salem³

2022

IJACSA

View full text Add to dashboard Cite

The globe was rocked by unprecedented levels of disruption, which had devastating effects on daily life, global health, and global economy. Since the COVID-19 epidemic started, methods for delivering accurate diagnoses for multicategory classification have been proposed in this work (COVID vs. normal vs. pneumonia). XceptionNet and Dense Net, two transfer learning pre-trained model networks, are employed in our CNN model. The low-level properties of the two DCNN structures were combined and used to a classifier for the final prediction. To get better results with unbalanced data, we used the GEV activation function (generalized extreme value) to augment the training dataset using data augmentation for validation accuracy, which allowed us to increase the training dataset while still maintaining validation accuracy with the output classifier. The model has been put through its paces in two distinct scenarios. In the first instance, the model was tested using Image Augmentation for train data and the GEV (generalized extreme value) function for output class, and it got a 94% accuracy second instance Model evaluations were conducted without data augmentation and yielded an accuracy rating of 95% for the output class.

show abstract

“…Furthermore, a comparison is made between the proposed architecture and the Teacher/Student structure by considering the same training set. Deep learning-based CNN and Res Net-18 techniques are explained in Aminuddin et al (2022) to identify the affected Chilli leaf. The authors achieved an accuracy of 97% in terms of performance by considering the original and augmented datasets.…”

Section: Introductionmentioning

confidence: 99%

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

Srinivasulu,

Maiti

2024

Eng. Res. Express

View full text Add to dashboard Cite

The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.

show abstract

An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset

Cited by 3 publications

References 23 publications

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

Contact Info

Product

Resources

About