Yousef Abbaspour GILANDEH, Sajad SABZI, Hossein JAVADIKIA

Patates Bitkisinde Yabani Otları Belirlemek için Yapay Görme Sisteminin Geliştirilmesi

Patates, tüm dünyada yaygın olarak kullanılan ürünlerden birisi olup sayısız besleyici özelliklere sahiptir. Tarım arazilerinde diğer bitkilerde olduğu gibi patateslerle birlikte farklı yabani otlar da yetişmektedir. Bu yabani otlar; su, ışık ve topraktaki besin maddeleri için ana ürünle rekabete girerek bitkilerin büyüme performansını düşürürler. Bu nedenle çalışmada, yabani ot tipini göz önüne alarak bölgeye özel püskürtme yapan, yapay sinir ağı-karınca koloni algoritması (ANN-ACO)’ndan oluşan hibrit sınıflandırıcıya sahip bir yapay görme sistemi geliştirilmiştir. Bu çalışmada patates bitkisi ile Chenopodium album, Polygonum aviculare L. ve Secale cereale L. olmak üzere üç yabancı ot çeşidi kullanılmıştır. Video çekim sisteminin merkezine bir dijital kamera (SAMSUNG, WB151F (CCD, 14,2 MP, 30f/s) yerleştirilmiştir. Bitkiler ve dijital kamera arasındaki mesafe 40 cm olarak sabitlenmiştir. Video çekimi için yalnızca 327 lux ışık yoğunluğundaki beyaz LED lambaları seçilmiştir. Önerilen sistemi değerlendirmek üzere filme almak için Kermanshah-Iran’da (boylam: 7.03°E, enlem: 4.22°N), bir Agria patates tarlasının 4 hektarlık alanı seçilmiştir. Gamma testi uygulanarak, 31 özellik arasından 5 özellik (YIQ renk uzayına karşılık gelen parlaklık ve renk tonu, Otomatik korelasyon, Kontrast ve Korelasyon) seçilmiştir. Hibrit ANN-ACO, radyal esas fonksiyonlu (RBF) yapay sinir ağı ve Diskriminant analizi (DA) içeren üç sınıflandırıcı kullanılarak yapılan test ve eğitme verileri için düzgün sınıflandırma doğruluğu değerleri sırasıyla % 99.6 ve % 98.13, % 97.24 ve % 91.23, % 69.8 ve % 70.8’dir. Sonuçlar, DA istatistiksel yönteminin doğruluğunun, hibrit ANN-ACO sınıflandırıcısından çok daha düşük olduğunu göstermiştir. Sonuç olarak, sunulan çalışmanın sonuçları herbisitlerin en uygun şekilde püskürtülebilmesi için yapay görme sisteminde kullanılabilir.

Developing a Machine Vision System to Detect Weeds from Potato Plant

Potato is one of the widely used products all over the world that has numerous nutritional properties. Similar to other 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.

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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
Wisaeng K (2013). A Comparison of decision tree algorithms for UCI repository classification. International Journal of Engineering Trends and Technology 4: 3393-3397
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
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
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
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
Marques O (2011). Practical image and video processing using matlab. John Wiley & Sons, Inc, Hoboken, New Jersey
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
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
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
Gonzalez R C, Woods R E & Eddins S L (2004). Digital Image Processing Using MATLAB. Prentice Hall, New York
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
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
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
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