No abstract

Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texturefeature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier.

Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and userfriendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household's food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households' data. Furthermore, the objective of the study was to enhance food security awareness among individuals.

The main objective of this study was to assess the machine vision (MV) techniques to classify six Asian rice varieties commonly named as Kachi-Kainat, Kachi-Toota, Kainat-Pakki, Super-Basmati-Kachi, Super-Basmati-Pakki, and Super-Maryam-Kainat (A1, A2, A3, A4, A5, and A6), mainly cultivated in Pakistan, China, India, Bangladesh, and neighboring countries. The sample of each selected rice variety contained 1800 grains, giving a total of 10800 (1800 × 6) grain samples. A cell phone camera captured the actual field digital images dataset in an open climate. All the captured images were enhanced and converted into the standard 8-bit gray-scale format. Six radius-based nonoverlapping regions of interest (ROI's) were taken on each captured image inducing a total of 3600 (6 × 600) ROI's image dataset. We have extracted Binary (B), Histogram (H), and Texture (T) features from each image. We converted these forty-three features for each image into 154800 (43 × 3600) feature vector (FV) space to discriminate rice varieties. After optimizing the FV, five MV classifiers, namely; LMT Tree (LMT-T), Meta Classifier via Regression (MCR), Meta Bagging (MB), Tree J48 (T-J48), and Meta Attribute Select Classifier (MAS-C), were deployed attaining the classification accuracies as 97.4%, 97.0%, 96.3%, 95.74%, and 95.2%, respectively. The maximum overall accuracy (MOA) observed was 97.4% by LMT-Tree.