A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Abstract

Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

“…One may wonder how these classification results compare toward the literature on weed detection in less dense culture cited in the introduction section [12][13][14][15][16][17][18][19][20][21]. The performance in this literature varies from 75% to 99% of good detection of weed.…”

“…Bayesian classifier was used in [16] for plant and weed discrimination. Shape, texture features [12,[17][18][19] or wavelet transform [20,21] coupled with various classifiers including support vector machine (SVM), relevance vector machine (RVM), fuzzy classifier, or random forests were also shown to provide successful pipelines to discriminate between plant and weeds.…”

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits

“…A screening and elimination of unnecessary features is essential to reduce feature quantity and increase data processing speed. Different types of algorithms have been utilized to select effective features for orchard management, including stepwise discriminant analysis [ 68 ], quadratic discriminant analysis [ 69 ], linear discriminant analysis [ 70 ], hybrid artificial neural networks-cultural algorithm (ANN-CA) [ 71 ], particle swarm optimization (PSO) based differential evolution method [ 72 ], kernel principal component analysis (KPCA) [ 73 ], and so on. Upon locating/accessing the target to be sprayed, spray volume at the nozzle is adjusted based on the size or quantity of the target present in the images captured from the orchards.…”

Tensor based statistical segmentation of green vegetation canopy images

“…In the learning-based approaches, both unsupervised and supervised machine learning algorithms were investigated for vegetation segmentation. In the literature as unsupervised methods that do not require any training phase, K-means clustering (Kumar & Prema, 2016); Prema & Murugan, 2016) and particle swarm optimization (PSO) based K-means clustering (PSO) (Bai et al, 2014) can be found. As supervised methods that previously require a training phase, decision trees (Guo, Rage, & Ninomiya, 2013;Riegler-Nurscher, Prankl, Bauer, Strauss, & Prankl, 2018) artificial neural networks (Potena, Nardi, & Pretto, 2017) and statistical based (Ruiz-Ruiz, Gómez-Gil, & Navas-Gracia, 2009;Zheng, Shi, & Zhang, 2010) methods can be found.…”