A neuronal classification system for plant leaves using genetic image segmentation

This paper demonstrates the use of radial basis networks (RBF), cellular neural networks (CNN) and genetic algorithm (GA) for automatic classication of plant leaves. A genetic neuronal system herein attempted to solve some of the inherent challenges facing current software being employed for plant leaf classication. The image segmentation module in this work was genetically optimized to bring salient features in the images of plants leaves used in this work. The combination of GA-based CNN with RBF in this work proved more ecient than the existing systems that use conventional edge operators such as Canny, LoG, Prewitt, and Sobel operators. The results herein showed that GA-based CNN edge detector outperforms other edge detector in terms of speed and classication accuracy

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Precision farming involves integration of different areas of disciplines to lower production costs and improve productivity. One major arm of precision farming or agriculture is the development of computer - based vision systems for automatic identification of plant species. This work involves application of k Nearest Neighbour (kNN) and genetic principal component analysis (GA - PCA) for the development of computer - based vision systems for automatic identification of plant species. As the first contribution, several image descriptors were extracted from the images of plants found in the Flavia data set. Lots of these image features are affine maps and amalgamation of such massive features in one study is a novel idea. These descriptors are Zernike Moments (ZM), Fourier Descriptors (FDs), Lengendre Moments (LM) Hu 7 Moments, Texture, Geometrical properties and colour features. The GA - PCA (1907 x 41) feature space improved the classification accuracy of kNN from 84.98% to 88.75%

A Genetic Algorithm-Based Feature Selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

Application of Cellular Neural Networks and Naive Bayes Classifier in Agriculture

This article describes the use of Cellular Neural Networks (a class of Ordinary Differential Equation (ODE)), Fourier Descriptors (FD) and NaiveBayes Classifier (NBC) for automatic identification of images of plant leaves. The novelty of this article is seen in the use of CNN for image segmentation and a combination FDs with NBC. The main advantage of the segmentation method is the computation speed compared with other edge operators such as canny, sobel, Laplacian of Gaussian (LoG). The results herein show the potential of the methods in this paper for examining different agricultural images and distinguishing between different crops and weeds in the agricultural system

On The Application Of Genetic Probabilistic Neural Networksand Cellular Neural Networks In Precision Agriculture

This article details the effect of Gaussian smoothing parameter (spread) on the performance of Probabilistic Neural Networks (PNN). Two (2) different Genetic Algorithms (GAs) were used to optimize the PNN spread in order to avoid under and over fitting. In this work there is a novel combination of Cellular Neural Networks (CNN), Probabilistic Neural Networks (PNN) and GA to address the present challenges on automatic identification of plant species. Such problems include misclassification species of plants that are similar in shapes and image segmentation speed. In this work, GA was used in both feature selection and PNN parameter optimization. The GA developed herein improved the performance of the PNN. This work serves as a framework for building image classification or pattern recognition system

Zernike moments and genetic algorithm : tutorial and application

Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89% accuracy

Integrating soil and plant tissue tests and using an artificial intelligence method for data modelling is likely to improve decisions for in-season nitrogen management

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis by about 2-3 months, depending when plant testing occurred. A positive side-effect of plant testing is to check whether any other nutrients, apart from nitrogen, are limiting yield or an N response. Secondly, this paper explores an AI method as a comparison to the traditional modelling technique to further improve the accuracy and to turn the model into a self-calibrating model. Unlike a statistical autoregression technique, the tested AI method has dynamic functions that can be used not only on time series data but also on data such as used here

A synthetic wheat l-system to accurately detect and visualise wheat head anomalies

Greater knowledge of wheat crop phenology and growth and improvements in measurement are beneficial to wheat agronomy and productivity. This is constrained by a lack of public plant datasets. Collecting plant data is expensive and time consuming and methods to augment this with synthetic data could address this issue. This paper describes a cost-effective and accurate Synthetic Wheat dataset which has been created by a novel L-system, based on technological advances in cameras and deep learning. The dataset images have been automatically created, categorised, masked and labelled, and used to successfully train a synthetic neural network. This network has been shown to accurately recognise wheat in pasture images taken from the Global Wheat dataset, which provides for the ongoing interest in the phenotyping of wheat characteristics around the world. The proven Mask R-CNN and Detectron2 frameworks have been used, and the created network is based on the public COCO format. The research question is “How can L-system knowledge be used to create an accurate synthetic wheat dataset and to make cost-effective wheat crop measurements?”

Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics