Developing a Machine Vision System to Detect Weeds from Potato Plant

Year 2018, Volume: 24 Issue: 1, 105 - 118, 31.03.2018

Sajad Sabzı Yousef Abbaspour Gılandeh , Hossein Javadıkıa

https://doi.org/10.15832/ankutbd.446402

Cited By: 4

Abstract

crops, different weeds grow along with potatoes in agricultural fields. These weeds reduce the performance of crops due to competing with them to absorb water, light, and nutrients from soil. Accordingly, in this study, a machine vision system with the hybrid artificial neural network-ant colony algorithm (ANN-ACO) classifier was developed for a site-specific spraying considering the weed type. Potato plant and three weed types including Chenopodium album, Polygonum aviculare L., and Secale cereale L. were used in this study. A digital camera (SAMSUNG WB151F (CCD, 14.2 MP, 30f/s) was placed in the center of the video acquisition system. The distance between plants and the digital camera was fixed at 40 cm. For video acquisition, only lamps of white LED with a light intensity of 327 lux were selected. For filming in order to evaluate the proposed system, a 4-hectare area of Agria potato fields in Kermanshah-Iran (longitude: 7.03°E; latitude: 4.22°N) was selected. Employing the Gamma test, among 31 features, 5 features (Luminance and Hue corresponding to YIQ color space, Autocorrelation, Contrast, and Correlation) were selected. The correct classification accuracy for testing and training data using three classifiers of the hybrid ANN-ACO, radial basis function (RBF) artificial neural network, and Discriminant analysis (DA) was 99.6% and 98.13%, 97.24% and 91.23%, and 69.8% and 70.8%, respectively. The results show that the accuracy of DA statistical method is much lower than that of the hybrid ANN-ACO classifier. Consequently, the results of the present study can be used in machine vision system for the optimum spraying of herbicides. 

Keywords

Classification; Machine vision; Gamma test; Precision farming; Site-specific sprayin

References

• Arribas J I, Sánchez-Ferrero G V, Ruiz-Ruiz G & GómezGil J (2011). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture 78: 9-18
• Bossu J, Gee C & Truchetet F (2008). Development of a machine vision system for a real time precision sprayer. Electronic Letters on Computer Vision and Image Analysis 7(3): 54-66
• Chen Y, Lin P, He Y & Xu Z (2011). Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds. Biosystems Engineering 109: 220-227
• Chowdhury S, Verma B & Stockwell D (2015). A novel texture feature based multiple classifier technique for roadside vegetation classification. Expert Systems with Applications 42: 5047-5055
• Gonzalez R C, Woods R E & Eddins S L (2004). Digital Image Processing Using MATLAB. Prentice Hall, New York
• Hlaing S H & Khaing A S (2014). Weed and crop segmentation and classification using area thresholding. International Journal of Research in Engineering and Technology 3: 375-382
• Liu H, Lee S H & Saunders C (2014). Development of a machine vision system for weed detection during both of off-season and in-season in broadacre no-tillage cropping lands. American Journal of Agricultural and Biological Sciences 9(2): 174-193
• Liu X, Du H, Wang G, Zhou S & Zhang H (2015). Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network. Computer Methods and Programs in Biomedicine 122: 47-55
• Marques O (2011). Practical image and video processing using matlab. John Wiley & Sons, Inc, Hoboken, New Jersey
• Montalvo M, Guerrero J M, Romeo J, Emmi L, Guijarro M & Pajares G (2013). Automatic expert system for weeds/crops identification in images from maize fields. Expert Systems with Applications 40: 75-82
• Mursalin M, Hossain M M, Noman M K & Azam M S (2013). Performance analysis among different classifier including naive bayes, support vector machine and C4.5 for utomatic weeds classification. Global Journal of Computer Science and Technology Graphics & Vision 8(3): 11-16
• Noori R, Karbassi A R, Moghaddamnia A, Han D, ZokaeiAshtiani M H, Farokhnia A & Gousheh M G (2011). Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology 401: 177-189
• Sen T & Mathur H D (2016). A new approach to solve Economic Dispatch problem using a Hybrid ACOABC-HS optimization algorithm. Electrical Power and Energy Systems 78: 735-744
• Wisaeng K (2013). A Comparison of decision tree algorithms for UCI repository classification. International Journal of Engineering Trends and Technology 4: 3393-3397
• Zhao F, Cai C, Huang S, He D & Zhu J (2009). Weed Seeds Recognition Using Locally Linear Embedding. In: 2009 International Conference on Test and Measurement, 5-6 December, Hong Kong, China, pp. 59-62