The role of hyperspectral imaging in precision agriculture has increased recently as a result of numerous financial and environmental benefits. Differentiating plant types and conditions is vital to precision agriculture as it helps detect diseases and stresses and optimise growth control. Techniques using hyperspectral images have been developed to study plant types and conditions. Although such techniques are becoming a common trend in precision agriculture, the task remains challenging, because of high image dimensionality, data volume, and sensitivity of the analysis techniques to seasonal or condition changes. The research described in this thesis is concerned with the analysis of hyperspectral data for plants of different conditions, or crop classification by means of advanced machine learning techniques. The main contribution of this research lies in three new approaches proposed to improve local and global classification performance of plant and crop classification in a controlled environment (i.e. laboratory), in which adaptive feature selection, ensemble learning, novelty detection, feature level, and decision level fusions are used. The first approach integrates feature selection and ensemble learning, while the second approach incorporates feature selection, ensemble learning, and novelty detection and the third approach merges different levels of image properties at both feature extraction and classifying decision stages. In the first approach, a feature-ensemble framework is proposed to enhance robustness and performance of the classification. The input data are divided into a number of pools using jackknife data split. In total, six feature selection algorithms are used in each pool simultaneously to select the best performing algorithm. The decisions of the retained algorithms are then used to form the final decision of the framework using a hard fusion scheme. This approach is particularly suited when all class labels are available. The second approach extends the framework into unbalanced data cases (e.g. missing labels, or samples, or both). A domain-based novelty detection (probabilistic one-class support vector machine) is used with regard to defining the domain of the available class samples (one class only) and how this domain deviated from unseen testing samples. In the third approach, advanced machine learning techniques are employed to identify distinctive features in both spectral and spatial domains of hyperspectral images. Texture properties are explored as spatial features in the sub-band images. These two levels of properties are then integrated into a proposed spectral-texture framework for studying plant conditions. Feature level, decision level, and feature-and-decision level fusion schemes are used in the third approach framework to improve local and overall classification performance. Performances of these three approaches were evaluated using various hyperspectral datasets in addition to benchmark databases. Results of these three approaches show significant improvements in classification performance as compared to empirical spectral indices and conventional analysis methods. The overall improvement rates, using several hyperspectral datasets under different conditions, are greater than 1%, 2.5%, and 2% for the first, second, and third approaches, respectively. The findings also indicate the usefulness of the proposed approaches for precision agriculture analysis. In addition, the experiments across various hyperspectral datasets show the validity and applicability of the proposed approaches to a wide range of condition monitoring applications.