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Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera's potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation. [ABSTRACT FROM AUTHOR]

Iris identification is a well‐known technology used to detect striking biometric identification procedures for recognizing human beings based on physical behaviour. The texture of the iris is unique and its anatomy varies from individual to individual. As we know, the physical features of human beings are unique, and they never change; this has led to a significant development in the field of iris recognition. Iris recognition tends to be a reliable domain of technology as it inherits the random variation of the data. In the proposed study of approach, we have designed and implemented a framework using various subsystems, where each phase relates to the other iris recognition system, and these stages are discussed as segmentation, normalisation, and feature encoding. The study is implemented using MATLAB where the results are outcast using the rapid application development (RAD) approach. We have applied the RAD domain, as it has an excellent computing power to generate expeditious results using complex coding, image processing toolbox, and high‐level programing methodology. Further, the performance of the technology is tested on two informational groups of eye images MMU Iris database, CASIA V1, CASIA V2, MICHE I, MICHE II iris database, and images captured by iPhone camera and Android phone. The emphasis on the current study of approach is to apply the proposed algorithm to achieve high performance with less ideal conditions. [ABSTRACT FROM AUTHOR]

This paper presents a system for automatic number plate recognition, implemented for devices running the iOS operating system. The methods used for number plate recognition are based on existing methods, but optimized for devices with low hardware resources. To solve the task of automatic number plate recognition we have divided it into the following subtasks: image acquisition, localization of the number plate position on the image and character detection. The first subtask is performed by the camera of an iPhone, the second one is done using image pre-processing methods and template matching. For the character recognition we are using a feed-forward artificial neural network. Each of these methods is presented along with its results. [ABSTRACT FROM AUTHOR]