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primary topic: Resource-efficient federated learning over IoAT for rice leaf disease classification

Resource-efficient federated learning over IoAT for rice leaf disease classification
type: AcademicArticle
type: Article
abstract: Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15 % of protein intake and 21 % of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99 %, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. …
date: 2024-05
volume: 221
is part of: Computers and Electronics in Agriculture
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
subject: Engineering

Computers and Electronics in Agriculture
type: Collection
issn: 01681699

Meenakshi Aggarwal
type: Person
given name: Meenakshi
family name: Aggarwal

Thomas Prola
type: Person
given name: Thomas
family name: Prola

Nitin Goyal
type: Person
given name: Nitin
family name: Goyal

Vikas Khullar
type: Person
given name: Vikas
family name: Khullar

HTML Summary of #17895 Resource-efficient federated learning over IoAT for rice leaf disease classification
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primary topic: Resource-efficient federated learning over IoAT for rice leaf disease classification

Engineering
type: Concept
preferred label: Ingeniería @es