Abstract Botanists use morphological features of leaves for aquatic and semi‐aquatic plants identification. In deep learning, the convolution process is efficacious to label images. Existing studies showed that, in deep learning‐based leaf identification, direct image pixel values are used. Leaf images have similar sizes and almost equal pixel values. So, the pixel‐based leaf naming procedure makes uncertainty in the feature results. The direct evaluation of image pixels for leaf image identification is not an effective method, because it is leading to a forgetting problem in continuous learning. Using the pre‐processed databases, researchers are achieving more than 99% accurate results using deep learning. However, the same model fails to reproduce the same result in databases. In this study, morphological features of aquatic and semi‐aquatic plant leaves are converted into digital descriptors to avoid uncertainty in the feature results. In this digital descriptor‐based deep learning method, morphological features of aquatic and semi‐aquatic plant leaves are used. This is equivalent to Botanists' morphological features‐based leaf identification technology. Morphological feature extraction and digital descriptor generation helped this model to achieve 95% accuracy. In this model, leaf morphological features are used for training, so this help to understand the leaf properties of other leaf databases.

Abstract An image is a set of numbers arranged in matrix form. The image feature extraction algorithm converts the input image into different numerical forms to extract the useful information from the input image and the selection of appropriate feature extraction algorithm is crucial for medicinal plant identification. In medicinal plants, the leaves are an available important resource of morphological features. Botanists use these morphological features of leaf images for medicinal plant identification. The existing leaf‐based medicinal plant identification strategies include shape, colour and texture features. In these methods, environmental factors directly influence the features and hence, the impact can be observed in the accuracy of the result. To overcome these limitations, we have proposed a unique morpho‐feature extraction algorithm (UMFEA) for accurate identification of medicinal plants. The UMFEA includes three sub‐algorithms for shape, apex, base, and vein features extraction. The proposed UMFEA is tested over Flavia, Swedish, Leaf and our databases. The performance comparison of UMFEA is done on different databases and the results obtained were remarkably good.

Abstract The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning.

Abstract Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.

Abstract Interpreting high‐resolution satellite imagery could be an expensive and time‐consuming task for human eyes. Computer Vision and Deep Learning techniques can help to solve this major problem by applying detection algorithms, which can ease the task of analysing such images for the benefit of humans. It can help in changing the way we comprehend and anticipate the economic activity around the world. Such techniques help us to observe the urban development in high security areas such as national and international borders. Constant progressions in improving and making satellites deployment, a cost‐effective process to strengthen the networks of satellite orbiting the earth is one of the reasons such tasks can be easily solved with the help of high‐resolution images. Current computer vision research works have achieved significant milestones in accuracy and speed but, there are still room for improvements. In this paper, we addressed some of these methods to bring them to a combined pipeline and proposed a set of improvements to further improve the speed and the accuracy of the detections. We proposed a unified framework, which combines several object detection algorithms and the state‐of‐art architecture of YoloV4 along with the TensorFlow object detection API. This framework can detect small and well as large objects with improved speed and accuracy by using two detectors for different scales. Evaluation ran on these high‐resolution images yield mAP of 85.6% F1‐score of 0.84.

Battery management systems in electric/hybrid vehicles are entirely based on accurate and reliable state estimation techniques. Consideration of combined effects of temperature, internal resistance rise, and capacity loss is essential to accurately estimate the current state of charge (SOC) of the battery. Most of the existing algorithms fail to capture the combined effects of the aforementioned variables. Moreover, an analysis of the inter-dependencies between the states and parameters related to the driving conditions is required. The main contribution of this paper is to introduce a hybrid and adaptive method of SOC estimation which captures the effects of initial SOC deviation and changes in SOC due to loss in capacity and rise in internal resistance. Modeling parameters related to the change in SOC, temperature, and capacity loss are continuously updated for the implemented battery model, whereby the equivalent circuit can be tuned to recent state of the battery. Simulation results are verified with experimental data from literature to prove the efficiency of the approach in terms of real-time applicability and accuracy.