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Abstract Wheat commands attention due to its significant impact on culture, nutrition, the economy, and the guarantee of food security. The anticipated rise in temperatures resulting from climate change is a key factor contributing to food insecurity, as it markedly reduces wheat harvests. Terminal heat stress mostly affects spike fertility in wheat, specifically influencing pollen fertility and anther morphology. This research especially focuses on the shape of anthers and examines the effects of heat stress. The DinoLite Microscope’s high-resolution images are used to measure the length and width of wheat anthers. By using object identification techniques, the research accurately measures the length and width of each anther in images, offering valuable insights into the differences between various wheat varieties. Furthermore, Deep Learning (DL) methodologies are utilized to enhance agriculture, specifically employing record categorization to advance plant breeding management. Given the ongoing challenges in agriculture, there is a belief that incorporating the latest technologies is crucial. The primary objective of this study is to explore how Deep Learning algorithms can be beneficial in categorizing agricultural records, particularly in monitoring and identifying variations in spring wheat germplasm. Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. LeNet demonstrates optimized accuracy in classifying the records, outperforming CNN by 52% and Inception-V3 by 70%. Moreover, Precision, Recall, and F1 Measure are utilized to ascertain accuracy levels. The investigation also enhances our comprehension of the distinct roles played by various genes in abiotic stress tolerance among diverse wheat varieties. The outcomes of the research hold the potential to transform agricultural practices by introducing a more effective, data-driven approach to plant breeding management.

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. • The methodology employs a hybrid model that combines LDA and LR for intrusion detection. • Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes. • The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network.

One of the challenges in image processing involves single image deraining (SID). Existing methods do not exploit images at multi-scales and often overlook spatial and channel information. These methods fail to account for different rain conditions, leading to challenges in effectively removing a wide range of rain streak patterns, such as diverse directional or dense streaks. Moreover, they often result in the loss of fine texture details or a blurred background in the process of eliminating rain streaks from images captured in heavy rain. Mostly state-of-the-art (SOTA) deraining models achieve higher performance in removing rain from rainy images but at the expense of a high number of parameters, which results in computational complexity and memory requirements. Furthermore, they also do not consider high-level visioned evaluation metrics to perform deep evaluations of the proposed models. In this paper, a simple lightweight network with few parameters and relatively shallow depth is proposed by fusing the traditional Gaussian-Laplacian pyramid technique with the attention module. We propose a novel attention-based lightweight pyramid network for image deraining and detection (LPN-IDD) to achieve better deraining performance. The proposed model includes a dual attention module integrated with the Gaussian-Laplacian pyramids network. In LPN-IDD, different levels of Laplacian pyramids can extract multi-scale features to adapt to different shapes and types of rain streaks. Residual and recursive blocks are used in each subnetwork along with dual attention blocks to resist the occlusion or texture feature while suppressing unnecessary features. Extensive experimentation performed on the SID synthetic and real-world datasets demonstrate the effectiveness of the proposed model in image deraining tasks. For deep evaluation of the proposed methodology, object detection models including faster-RCNN, YOLO-V3, YOLO-V7 are used along with full reference evaluation metrics.

Heterogeneity is the key aspect of complex networks and smart devices for using it as nature of live streams. The heterogeneous stream-disk join is a significant research topic in real-time processing applications because it can directly affect the data analytics. Multiple issues, including stream loss, scalability, disk access cost, and data accuracy, should be considered during heterogeneous stream-disk join transformation. In this work we overcome these issues by introducing a distributed heterogeneous stream-disk join architecture (DHSDJArch) which can prevent stream data loss as well as maintaining balance between heterogeneous distributed data sources and accuracy of stream-disk join. A four phased distributed architecture is proposed for the multi-objective optimization to transform heterogeneous incomplete stream. To prevent stream loss, configuration of log retention is proposed based on the characteristics of distributed event streaming platform (DESP). Specifically, two transformations are proposed to pre-process heterogeneous streams and to join pre-processed stream with distributed disk data by performing real-time disk access while compensating the differences between data sources and streaming application, respectively. We conduct comprehensive experimental study on real datasets to verify the performance of proposed architecture in terms of accuracy, log retention policy, scaling, stability and cloud data storage.