Detection of Infected Leaves and Botanical Diseases using Curvelet Transform

Abstract

The study of plants is known as botany and for any botanist it is a daily routine work to examine various plants in their research lab. This research efforts an image processingbased algorithm for extracting the region of interest (ROI) from plant leaf in order to classify the specie and to recognize the particular botanical disease as well. Moreover, this paper addresses the implementation of curvelet transform on subdivided leaf images in order to compute the related information and train the support vector machine (SVM) classifier to execute better results. Furthermore, the paper presents a comparative analysis of existing and proposed algorithm for species and botanical diseases recognition over the dataset of leaves. The proposed multi-dimensional curvelet transform based algorithm provides relatively greater accuracy of 93.5% with leaves dataset.

Weed Detection Method Based on Lightweight and Contextual Information Fusion

“…Currently, optical technologies encompass optical detection techniques, digital image processing, curvelet transform detection, and computer vision detection techniques, such as hyperspectral imaging technology and terahertz spectroscopy imaging, which have found widespread applications and development in object detection [7][8][9][10]. Optical technologies have been employed extensively, including the use of digital image processing for fruit grading and sorting [11], and curvelet transform detection for identifying plants infected with diseases [12]. However, these methods are associated with high detection costs or limited applicability.…”