Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural Universit

A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the bccr-segset dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection. © 2020 The Author(s) 2020

Plant discrimination by Support Vector Machine classifier based on spectral reflectance

Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different wavelengths. A testbed is especially built to collect the spectral reflectance properties of corn (as a crop) and silver beet (as a weed) at 635 nm, 685 nm, and 785 nm, at a speed of 7.2 km/h. Results show that the use of the Gaussian-kernel SVM method, in conjunction with either raw reflected intensities or NDVI values as inputs, provides better discrimination accuracy than that attained using the discrete NDVI-based aggregation algorithm. Experimental results carried out in laboratory conditions demonstrate that the developed Gaussian SVM algorithms can classify corn and silver beet with corn/silver-beet discrimination accuracies of 97%, whereas the maximum accuracy attained using the conventional NDVI-based method does not exceed 70%

Simulation of high fidelity control system designs using parallel architectures and floating point FPGA computing

Execution of Real Time simulation models is crucial in control systems but rarely achieved for highly complex feedback models. On the other hand, the use of Field Programmable Gate Arrays (FPGA) technology is proven to achieve execution speeds faster than real time for high fidelity models. However, as current FPGA applications are specialised and tool sets do not support basic control systems floating point blocks, significant effort is invested in order to incorporate new designs. These are typically non-intuitive, constructed and optimised manually. In order to overcome these difficulties, this thesis offers a standalone solution for simulation of control system designs using FPGAs. This is based on a floating point library of re-usable Hardware Descriptive Language (HDL) components, under System Generator toolbox, in Simulink. Also, extended research was performed in collaboration with Jaguar Land Rover, Rolls-Royce and Goodrich in order to underline general practices and main limitations of current methods found in Automotive and Aerospace Industries. The first contribution is a modelling design suite for floating point HDL control systems applications which reduces the design time to that of standard Simulink control systems simulation models. The most efficient FPGA design implementation is discussed. The presented methods are based on an extensive range of HDL design paths which assure the efficiency of the generated HDL structures, including comparisons not explored in the current literature. Contributions are offered for one of the major challenges found in generic FPGA implementations: the optimisation of the pipelining stages. A semi-automated throughput optimisation process was constructed on a rigorous mathematical model. Furthermore, the transition from serial to parallel architectures represents a considerable challenge due to an overwhelming number of unexplored options and conflicting factors. The work presented achieves the first reported complete parallelisation characterisation for generic MIMO L T1 state space systems using standalone FPGA implementations. This allows computational architectures to be split into most of the feasible combinations of serial and parallel FPGA computing blocks. Automatic optimisations of latency, occupied FPGA area and execution speed are attained and justified in respect to an increased number of possible implementations. These contributions are combined to offer a complete package for high fidelity control systems implementations. Results given by generic complex test case studies show a consistent execution time speed-up when compared to other industry based available technologies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. Keywords: Plant discrimination, Classification, LBP, PCA and SV