A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

Anari, Maryam Saberi

doi:10.1155/2022/6504616

Cited by 44 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of rice disease recognition, this approach is particularly valuable due to the limited availability of large, annotated datasets specific to this task. This approach mitigates issues associated with overfitting when training on small datasets and reduces the time needed for training on large datasets ( Fan et al., 2022 ; Saberi Anari, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Yuan,

Xia,

Tian

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage.

show abstract

Section: Methodsmentioning

confidence: 99%

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Yuan,

Xia,

Tian

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The agriculture sector needs efficient and error-free assessment to examine healthy goods from defective parts. The study [11] suggests a very good structure that can be used to categorize various leaf diseases in plants and fruits during the feature extraction stage. To extract certain characteristics, model engineering is utilized.…”

Section: Related Workmentioning

confidence: 99%

Development of an IoT integrated decision support system for Romanian vineyard management

Orza¹,

Bosoc²,

Balaceanu³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

View full text Add to dashboard Cite

The smart agriculture domain refers to the usage of technologies such as the Internet of Things (IoT), sensors, location systems, robots and Artificial Intelligence implemented on farms. The goal is to increase the quality and quantity of crop production while motivating viticulturists to implement new technologies as well as streamline production costs. The study's purpose is to monitor environmental conditions (soil temperature and humidity, air temperature and humidity) and to analyse diseased leaves from the vineyard using an intelligent, integrated decision support system for vineyard management that is based on the IoT end-to-end solution. This method suggests placing IoT sensors inside wine crops to measure environmental conditions, which are then sent and stored in the Cloud. Also, an Unmanned Aerial Vehicle (UAV) is used to capture images containing healthy and diseased leaves (powdery mildew, downy mildew, gray rot) from various regions of the vineyard. These images are analysed by implementing a Machine Learning algorithm that detects the state of the leaf. Following the analysis, the results are presented as graphs and charts, allowing the viticulturist to proceed with the necessary actions.

show abstract

“…There are several approaches to machine learning that can be used for cotton leaf disease identification, including [9,10]:…”

Section: Model Evaluation and Validation Error Analysis And Diagnosismentioning

confidence: 99%

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Govindasamy,

Jayaraj

2023

Preprint

View full text Add to dashboard Cite

This research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction. Cotton leaf diseases significantly threaten crop productivity, requiring timely detection and precise prediction for effective disease management. The proposed TFSO-HMM framework combines the strengths of Tenacious Fish Swarm Optimization (TFSO) and the Hidden Markov Model (HMM) to address the challenges associated with disease identification and yield prediction in cotton plants. TFSO, a nature-inspired optimization algorithm, optimizes the classification process, enhancing the accuracy of disease identification. By harnessing the collective intelligence of fish swarms, TFSO intelligently explores the search space to identify the optimal solution. The selected information is then incorporated into the HMM framework, which captures the temporal dependencies in disease progression and yield prediction. HMM's sequential modelling approach facilitates understanding the dynamic behaviour of cotton leaf diseases over time, leading to more accurate predictions. Experimental results on a comprehensive dataset demonstrate the superior performance of the TFSO-HMM method over existing approaches in terms of accuracy and predictive capability. The augmented accuracy achieved through TFSO-HMM enables early detection and precise prediction of cotton leaf diseases, enabling timely interventions for disease management and maximizing crop yield.

show abstract

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

Cited by 44 publications

References 50 publications

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Development of an IoT integrated decision support system for Romanian vineyard management

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Contact Info

Product

Resources

About