Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

Parez, Sana; Dilshad, Naqqash; Alghamdi, Norah Saleh; Alanazi, Turki M.; Lee, Jong Weon

doi:10.3390/s23156949

Cited by 28 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of sophisticated video surveillance, CNNs are employed for a wide variety of tasks, including plant disease detection [43][44][45], video summarizing [46], and crowd counting [47], as well as object detection [48] and vehicle re-identification [49]. The CNN structure consists of three major components: the convolution layer (CL), the pooling layer (PL), and the fully linked Layer (FL).…”

Section: Deep Features Extractionmentioning

confidence: 99%

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem,

Dilshad,

Alghamdi

et al. 2023

Smart Cities

Self Cite

View full text Add to dashboard Cite

The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.

show abstract

Section: Deep Features Extractionmentioning

confidence: 99%

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem,

Dilshad,

Alghamdi

et al. 2023

Smart Cities

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given the potential loss of important information with CNN models, De Silva Malithi et al [14] combined a CNN with ViT, achieving an 83.3% accuracy rate. To enhance accuracy, Parez Sana et al [15] proposed the green vision transformer technique, employing ViT to reduce model parameters and improve accuracy, demonstrating real-time processing capability. Thai Huy-Tan et al [16] designed the FormerLeaf model based on ViT for plant disease detection.…”

Section: Introductionmentioning

confidence: 99%

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Lu,

Zheng

et al. 2024

Plants

View full text Add to dashboard Cite

In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method’s effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method’s capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

show abstract

“…Deep learning provides numerous applications in various fields, such as segmentation, detection, and classification [2,3,23,24]. Over the past few years, using convolutional neural network (CNN)-based techniques for fire detection has gained popularity, proving effective in uncertain and certain environmental surveillance systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

Farman,

Nasralla,

Khattak

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Fire detection employing vision sensors has drawn significant attention within the computer vision community, primarily due to its practicality and utility. Previous research predominantly relied on basic color features, a methodology that has since been surpassed by adopting deep learning models for enhanced accuracy. Nevertheless, the persistence of false alarms and increased computational demands remains challenging. Furthermore, contemporary feed-forward neural networks face difficulties stemming from their initialization and weight allocation processes, often resulting in vanishing-gradient issues that hinder convergence. This investigation recognizes the considerable challenges and introduces the cost-effective Encoded EfficientNet (E-EFNet) model. This model demonstrates exceptional proficiency in fire recognition while concurrently mitigating the incidence of false alarms. E-EFNet leverages the lightweight EfficientNetB0 as a foundational feature extractor, augmented by a series of stacked autoencoders for refined feature extraction before the final classification phase. In contrast to conventional linear connections, E-EFNet adopts dense connections, significantly enhancing its effectiveness in identifying fire-related scenes. We employ a randomized weight initialization strategy to mitigate the vexing problem of vanishing gradients and expedite convergence. Comprehensive evaluation against contemporary state-of-the-art benchmarks reaffirms E-EFNet’s superior recognition capabilities. The proposed model outperformed state-of-the-art approaches in accuracy over the Foggia and Yar datasets by achieving a higher accuracy of 0.31 and 0.40, respectively, and its adaptability for efficient inferencing on edge devices. Our study thoroughly assesses various deep models before ultimately selecting E-EFNet as the optimal solution for these pressing challenges in fire detection.

show abstract

Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

Cited by 28 publications

References 36 publications

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

Contact Info

Product

Resources

About