In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning repository database has been classified using different machine learning algorithms including Multilayer Perceptron, k-NN, J48, Naïve Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in the classification of forest type with 27 spectral features.

This study investigates the distribution and carbon content of organic soils in Latvia, leveraging machine learning techniques alongside remote sensing and National Forest Inventory (NFI) data to enhance the precision of organic soil mapping. Our approach integrates data from various sources, including airborne laser scanning (ALS) data, digital elevation models (DEM), depth-to-water (DTW) and wet area maps (WAM), and historical organic soil data. By classifying over 24,000 soil probing measurements across Latvia into distinct peat layer thickness categories, we develop a machine learning model that categorizes the thickness of the organic layer with notable accuracy. Our findings indicate that the model, particularly when employing the xgbTREE algorithm and over-sampling method, successfully identifies areas with peat layers thicker than 40 cm, demonstrating a significant improvement over traditional mapping methods. The study reveals an underestimation of organic soil coverage in Latvia by previous estimates, suggesting a broader distribution than recognized, with the model achieving an accuracy of 0.86 and a kappa value of 0.67. This research not only underscores the efficacy of integrating machine learning and remote sensing for soil mapping but also highlights the critical role of accurate data and models in determining organic soil distribution. The insights gained from this study are vital for policy-making and environmental planning, offering a more detailed understanding of Latviaʼs peatland resources and their conservation needs.

Globally crop pathogens and pests cause significant yield and quality losses in agriculture production systems. Foliar fungal diseases of small grain cereals are economically among the most important diseases worldwide and in the Baltics. Finding an effective, reliable, and easily accessible method for plant disease diagnosis still presents a challenge. Currently used methods include visual examination of the affected plant, morphological characterization of isolated pathogens and different molecular, and serological methods. All of these methods have important limitations, especially for large-area applications. Hyperspectral imaging is a promising technique to assess fungal diseases of plants, as it is a non-invasive, indirect detection method, where the plant’s responses to the biotic stress are identified as an indicator of the disease. Hyperspectral measurements can reveal a relationship between the spectral reflectance properties of plants and their structural characteristics, pigment concentrations, water level, etc., which are considerably influenced by biotic plant stress. Despite the high accuracy of the information obtained from hyperspectral detectors, the interpretation is still problematic, as it is influenced by various circumstances: noise level, lighting conditions, abiotic stress level, a complex interaction of the genotype and the environment, etc. The application of hyperspectral imaging in everyday farming practice will potentially allow farmers to obtain timely and precise information about the development of diseases and affected areas. This review provides an introduction into issues of hyperspectral imaging and data analysis and explores the published reports of worldwide research on the use of hyperspectral analysis in the detection of foliar fungal diseases of small-grain cereals.

The paper presents the results of the research on oil spill detection using machine learning methods such as Support Vector Machine (SVM) for classification of infrared images and Logistic regression for water quality parameters. This paper focuses on real time detection of oil spills using infrared images and water quality data obtained by RPA equipped with multi-sensor payload. The developed Naïve Bayes (NB), SVM and Logistic regression classification models for prediction of oil spill have been successfully tested in real experiment conditions. All developed classification models were tuned using grid search method and main tuning parameters to determine the optimal parameters. The proposed complex algorithm for identification of oil spills using infrared images and water quality parameters is evaluated by experiments in real environment conditions. The proposed algorithm is based on the binary SVM and NB classification of infrared images and the classification of water quality parameters using the machine learning method logistic regression allows to rapidly and with high accuracy identify any oil pollution of water. Proposed complex algorithm achieves higher accuracy and efficiency; moreover, the developed machine learning models will further reduce the probability of human error and save man-hours of work.