A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms

Abstract Precision Agriculture is an area that can benefit from the latest advances in expert systems. One of the goals is to detect and remove weeds intelligently, so that herbicides are only sprayed in areas where weeds exist. By reducing the amount of herbicides used, the risk of contamination of crop and water resources is avoided, which could produce harmful environmental effects. In this paper, a new computer vision based expert system is presented for identifying potato plants and three different kinds of weeds (Secale cereale L., Polygonum aviculare L. and Xanthium strumarium L.) in order to perform site-specific spraying. The videos were recorded from two Marfona potato crops, with a total area of 4 ha located in Kermanshah–Iran (34°20′17.203′′N, 46°19′56.807′′E), taken with a moving platform with a speed of 0.13 m/s, under outdoor lighting conditions. Applying image processing, 3459 objects were extracted and used to train and test the classifiers. 126 color features and 60 texture features were extracted from each object. The main contribution of the proposed approach was the application of two metaheuristic algorithms to optimize the performance of a neural network classifier: first, the cultural algorithm is used to select the five most effective features, in order to improve computational efficiency; then, the harmony search algorithm is applied to find the optimal configuration of the network. This approach has been compared with a statistical method based on linear discriminant analysis. The experimental results show that the proposed expert system achieves an excellent identification accuracy of 98.38%, requiring less than 0.8 s of execution on an average PC.

[1]  J. M. González-Esquiva,et al.  Optimal color space selection method for plant/soil segmentation in agriculture , 2016, Comput. Electron. Agric..

[2]  Ross A Frick,et al.  Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis , 2011, Plant Methods.

[3]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Aimen Aakif,et al.  Automatic classification of plants based on their leaves , 2015 .

[5]  Muralindran Mariappan,et al.  Genetic Algorithm Optimization and Feature Selection for a Support Vector Machine Weed Recognition System in Malaysia at Critical Stage of Development , 2014 .

[6]  Gang Liu,et al.  Development of agricultural implement system based on machine vision and fuzzy control , 2015, Comput. Electron. Agric..

[7]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[8]  Robert G. Reynolds,et al.  A novel hybrid Cultural Algorithms framework with trajectory-based search for global numerical optimization , 2016, Inf. Sci..

[9]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[10]  Robert L. Zimdahl,et al.  Fundamentals of Weed Science , 1993 .

[11]  Shu-Hsien Liao,et al.  Expert system methodologies and applications - a decade review from 1995 to 2004 , 2005, Expert Syst. Appl..

[12]  Min Huang,et al.  Maize and weed classification using color indices with support vector data description in outdoor fields , 2017, Comput. Electron. Agric..

[13]  Gonzalo Pajares,et al.  Automatic expert system for weeds/crops identification in images from maize fields , 2013, Expert Syst. Appl..

[14]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[15]  Victor Ciesielski,et al.  Rice leaf detection with genetic programming , 2013, 2013 IEEE Congress on Evolutionary Computation.

[16]  George E. Meyer,et al.  Plant species identification, size, and enumeration using machine vision techniques on near-binary images , 1993, Other Conferences.

[17]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[18]  T. Kataoka,et al.  Crop growth estimation system using machine vision , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[19]  M. F. Kocher,et al.  Textural imaging and discriminant analysis for distinguishing weeds for spot spraying , 1998 .

[20]  Noboru Ohnishi,et al.  A New Color-based Lawn Weed Detection Method and Its Integration with Texture-based Methods: A Hybrid Approach , 2011 .

[21]  João Paulo Papa,et al.  Aquatic weed automatic classification using machine learning techniques , 2012 .

[22]  J. L. Gonzalez-Andujar.  Expert system for pests, diseases and weeds identification in olive crops , 2009, Expert Syst. Appl..

[23]  Xiao-Qian Chen,et al.  Classification of farmland images based on color features , 2015, J. Vis. Commun. Image Represent..

[24]  J. M. Molina-Martínez,et al.  A new portable application for automatic segmentation of plants in agriculture , 2017 .

[25]  Adel Bakhshipour,et al.  Weed segmentation using texture features extracted from wavelet sub-images , 2017 .

[26]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[27]  Dimitrios Moshou,et al.  Active learning system for weed species recognition based on hyperspectral sensing , 2016 .