Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral imaging (HSI) has been widely used in a wide range of real-world application areas due to the in-depth spectral information stored within each pixel. Noticeably, the detailed features, i.e., a nonlinear correlation between the obtained spectral data and the correlating HSI data object, generate efficient classification results that are complex for traditional techniques. Deep learning (DL) has recently been validated as an influential feature extractor that efficiently identifies the nonlinear issues that have arisen in various computer vision challenges. This motivates using DL for HSIC, which shows promising results. This survey provides a brief description of DL for HSIC and compares cutting-edge methodologies in the field. We will first summarize the key challenges for HSIC, and then, we will discuss the superiority of DL and DL ensemble in addressing these issues. In this article, we divide state-of-the-art DL methodologies and DL with ensemble into spectral features, spatial features, and combined spatial–spectral features in order to comprehensively and critically evaluate the progress (future research directions as well) of such methodologies for HSIC. Furthermore, we will take into account that DL involves a substantial percentage of labeled training images, whereas obtaining such a number for HSI is time and cost consuming. As a result, this survey describes some methodologies for improving the classification performance of DL techniques, which can serve as future recommendations.

The increase in the internal temperature of high voltage electrical instruments is due to a variety of factors, particularly, contact problems; environmental factors; unbalanced loads; and cracks in the high voltage current transformers, voltage transformers, insulators, or terminal junctions. This increase in the internal temperature can cause unusual disturbances and damage to high voltage electrical equipment. Therefore, early prevention measures of thermal anomalies in equipment are necessary to prevent high voltage equipment failure that might shut down the whole grid system. In this article, we propose a novel non-destructive approach to defect analysis in high voltage equipment by taking advantage of the infrared thermography and the deep learning (DL) approach from the machine learning paradigm. The infrared images of the components were captured using the FLIR T630 without disturbing the operations of the power grid. In the first stage, rich features maps from the convolutional layers of the AlexNet pretrained model were extracted. After feature extraction, the random forest (RF) and support vector machines (SVM) were trained for learning of the defective and non-defective high voltage electrical equipment. In an experimental analysis, the RF optimally learned the separation between defective and non-defective equipment with greater than 96% accuracy, outperforming all the other comparative approaches for deep and nondeep features. The proposed approach based on the RF is reliable and shows its efficacy for fault detection in high voltage electrical equipment.

Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

Pernicious insects and plant diseases threaten the food science and agriculture sector. Therefore, diagnosis and detection of such diseases are essential. Plant disease detection and classification is a much‐developed research area due to enormous development in machine learning (ML). Over the last ten years, computer vision researchers proposed different algorithms for plant disease identification using ML. This paper proposes an end‐to‐end semantic leaf segmentation model for plant disease identification. Our model uses a deep convolutional neural network based on semantic segmentation (SS). The proposed algorithm highlights diseased and healthy parts and allows the classification of ten different diseases affecting a specific plant leaf. The model successfully highlights the foreground (leaf) and background (nonleaf) regions through SS, identifying regions as healthy and diseased parts. As the semantic label is provided by the proposed method for each pixel, the information about how much area of a specific leaf is affected due to a disease is also estimated. We use tomato plant leaves as a test case in our work. We test the proposed CNN‐based model on the publicly available database, PlantVillage. Along with PlantVillage, we also collected a dataset of twenty thousand images and tested our framework on it. Our proposed model obtained an average accuracy of 97.6%, which shows substantial improvement in performance on the same dataset compared to previous results.