Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

Antico, Thalita Mendonça; Rodrigues, Larissa Ferreira; Moreira, Rodrigo Martins

doi:10.5753/eniac.2022.227293

Cited by 20 publications

(6 citation statements)

References 34 publications

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“…Antico et al [20] presented a study showcasing how FL effectively addresses challenges while also emphasizing the obstacles that demand attention. Their research involved the implementation of a federated learning framework utilizing five Convolutional Neural Network (CNN) models.…”

Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

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Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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“…Recent studies on federated learning, including concepts, challenges, privacy and security, and future research directions have been conducted in [13], [11], [25]. Growing steadily in recent years, federated learning has been applied to solve different types of problems in several domains, including medical [26], [27], [28], distributed networks and systems [24], [29], [30], Internet of Things (IoT) [31], and very recently in the agricultural domain [32], [33], [34].…”

Section: Federated Learning (Fl)mentioning

confidence: 99%

Image-based crop disease detection with federated learning

KABALA,

HAFIANE,

BOBELIN

et al. 2023

Preprint

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Crop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to develop automated crop disease detection systems. However, centralized approach to data collection and model training induces challenges in terms of data privacy, availability, and transfer costs. To address these challenges, federated learning appears to be a promising solution. In this paper, we explored the application of federated learning for crop disease classification using image analysis. We developed and studied convolutional neural network (CNN) models and those based on attention mechanisms, in this case vision transformers (ViT), using federated learning, leveraging an open access image dataset from the "PlantVillage" platform. Experiments conducted concluded that the performance of models trained by federated learning is influenced by the number of learners involved, the number of communication rounds, the number of local iterations and the quality of the data. With the objective of highlighting the potential of federated learning in crop disease classification, among the CNN models tested, ResNet50 performed better in several experiments than the other models, and proved to be an optimal choice, but also the most suitable for a federated learning scenario. The ViT_B16 and ViT_B32 Vision Transformers require more computational time, making them less suitable in a federated learning scenario, where computational time and communication costs are key parameters. The paper provides a state-of-the-art analysis, presents our methodology and experimental results, and concludes with ideas and future directions for our research on using federated learning in the context of crop disease classification.

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“…Atico et al [55] evaluated the performance of FL system with five CNNs trained in a distributed environment and measured their training time compared to their classification performance. FL was efficient in predicting crop leaf diseases from images.…”

Section: Use Cases Of Fl Applications In Agriculturementioning

confidence: 99%

Section: Challenges Of Production Fl Systemsmentioning

confidence: 99%

“…All considered FL systems in agriculture shown in Table 3 have small number of clients, but production FL systems can involve millions of devices in one netwoek. Such production FL systems faces a few challenges [55]: cost communication, heterogeneity, statistical heterogeneity, and privacy concerns. New challenges are productionizing and benchmarking in federated contexts are described ina [63].…”

Section: Challenges Of Production Fl Systemsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Federated Learning in Agriculture

Žalik,

Žalik

2023

Sensors

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Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems. We compare the types of data partitioning and types of FL (horizontal partitioning and horizontal FL, vertical partitioning and vertical FL, and hybrid partitioning and transfer FL), architectures (centralized and decentralized), levels of federation (cross-device and cross-silo), and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. We also briefly review how the communication challenge is solved by different approaches. This work is useful for gaining an overview of the FL techniques used in agriculture and the progress made in this field.

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Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

Cited by 20 publications

References 34 publications

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Image-based crop disease detection with federated learning

A Review of Federated Learning in Agriculture

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