Aman Singh

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.

The authors would like to make the following corrections about the published paper [...]

The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission.