Abstract

The objective is to create and evaluate an image processing application designed for identifying leaf diseases and classifying soil types, utilizing a combination of Linear-Non-Linear (L-NL) Classification Techniques and the Vector Machine Learning (VML) Algorithm. We go over the methods created and put to the test for automating the process of classifying soil types and leaf diseases. The majority of methods begin by segmenting the measured signals from the acquired data using a segmentation algorithm. Next, effective extraction techniques are employed to extract the valuable information from these segments. Certain classifiers that are specifically designed to assign classes to these segments are constructed using the measured data and features that were extracted from the collected samples; these classifiers may make use of VML methods. Cone penetration testing collected data was utilised in the traditional approach for categorising subsurface soil, and positive outcomes were attained. In the past, classification systems required a domain expert to observe the trends and size of the segmented signals in addition to any accessible a priori knowledge. The suggested methods for employing image processing to automate this classification process are also included in this paper. Boundary energy approach can be used to extract the prominent aspects of the observed segments from a segmentation algorithm, for example. Dedicated linear and non-linear classifiers assign classes to the observed segments based on the measured data and attributes taken from the gathered data of soil and damaged and healthy plant leaves. Then, VML algorithm classification is applied. In summary, the procedure begins with the application of feature extraction methods in image processing to identify the characteristics of soil samples and leaf images. Subsequently, a database of these sample images is created. The classification of soil types and leaf diseases is then performed based on their vector features using the VML algorithm.

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