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

Abstract

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.

Herbal Leaf Classification Using Deep Learning Model Efficientnetv2b0

“…Another approach that can be utilized in completing image classification is using the deep learning method, which is a computational model that adopts the working pattern of human neural networks, employing numerous layers of non-linear processing units for feature extraction and manipulation [9]. Similar to other studies, which used the Convolutional Neural Network (CNN) deep learning model with the EfficientNetB3 architecture to classify rice plant illnesses, the results showed an accuracy of 99% on testing data and a training loss value of 0.012 [10]. The highest accuracy value was obtained at the 30th epoch.…”

Sigatoka and Xanthomonas Banana Leaf Disease Detection Via Transfer Learning

“…In another study, the focus was on enhancing the efficiency of plant disease identification using CNN-based pre-trained models [6] disease detection [9,10]. They used federated transfer learning and got 99% accuracy for rice leaf.…”

Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments

“…Peng J et al [11] proposed the RiceDRA-Net model, which uses a 3 × 3 convolution kernel and more dense connections to reduce information loss and achieves high accuracy on rice leaf disease datasets in complex scenarios. M Aggarwal et al [12] proposed a framework called Federated Transfer Learning (F-TL) for rice leaf disease classification across multiple clients and databases. The framework adopts federated learning and can train shared models on distributed devices or servers without directly transmitting or concentrating raw data.…”