Application of Neural Networks for Seed Germination Assessment

Transcription

1 Application of Neural Networks for Seed Germination Assessment MIROLYUB MLADENOV, MARTIN DEJANOV Department of Automatics, Information and Control Engineering University of Rouse 8 Studentska Str., 7017 Rousse BULGARIA mladenov@ru.acad.bg, mdejanov@ru.acad.bg, Abstract: This paper is focused on some key problems, related to the development of a new technology for seed germination assessment using computer vision. An approach for germinated seed image segmentation based on artificial neural networks is proposed. Тhe possibility to use standard neural networks for seed, germ and roots zones extraction is analyzed. A modified RBFN is developed. The accuracy of realized procedures is evaluated. Key-Words: neural networks, computer vision, image analysis, germination, segmentation, color and texture models. 1 Introduction The seeds are one of the important plant agricultural products. The quality and quantity of the plant crops depend on the seed quality. The seed quality is evaluated on the basis of specific sowing properties like purity, germination, humidity, infections, vitality, vigor, authenticity, etc. The diversity of the seed quality factors, as well as the difficulties in their quantitative/qualitative assessment, makes the quality assessment procedures materially difficult. A big part of existing seed quality assessment technologies and tools is primitive and does not change for years. The key role in these technologies has the expert - his knowledge, experience and possibilities for result interpretation. This defines the assessment nature subjective, slow and expensive. There is a tendency for development of new automatic technologies for seed quality assessment. Computer vision systems are in the basis of these technologies. This is not accidentally. A big part of seed quality factors is evaluated by the expert on the grounds of different visible features. Using image processing methods can be detected different seed quality features like seed shape [1,,11,1], morphological [3,6,1], color [4,7,14,17] and texture features [8,18], the presence of different injuries [3,1,13] and diseases [18] and can be evaluated different seed sowing properties like purity [1,1,13,17], germination [1,19], infections [18], authenticity [,5,6,15], etc. The neural networks are widely used in these analyses [,3,5,9,11,14,16]. They give a possibility to develop more powerful, effective and universal tools for solving different classification problems, related to the seed sowing properties assessment. This paper includes results from an investigation, concerning the usage of color and texture models, as well as neural networks for seed germination assessment based on a computer vision system. Problem Formulation The seed germination is one of the main seed sowing properties. The complete germination assessment by a computer vision system is a difficult and complex problem. The development of procedures for germination assessment is related to the following main problems: germinated seed zone extraction; germinated seed zone segmentation and detection of seed, germ and roots zones; determination of the segment geometrical characteristics; seed classification in quality groups according to the normative requirements. The investigation aims to determine which image features (color, texture) and which image processing tools give an effective solution for seed segment zone extraction. 3 Problem Solution 3.1 Image homogenous zones extraction Color and texture models The results in related papers [7,8,11,14] show, that an effective result concerning the seed quality assessment can be obtained using image zone segmentation based on colour and texture features. For homogenous zones extraction are used 4 colour models (HSI (Hue Saturation Intensity), XYZ, NTSC(National Television Systems Committee), YCbCr) and the following new texture models: ISBN: ISSN:

2 1) First model: R G B R G B d1 = ; d = ; d 3 = ; R + G + B R + G + B R + G + B d1 + d + d 3 = 0 (1) ) Second model: R B G О1 = ; O = ; O3 = ; G R B () I = R + G + B;O1 *O * O3 = 1 3) Third model: R B G О 1 = ; O = ; O3 = ; O1 *O *O3 = 1 (3) G R B 4) Fourth model: 3*( R + m * G + n * B) Tk = ; ( R + G + B) *(1 + m + n), (4) m G B K r = ; m = ; n = n R R where R, G and B are the coordinates of the basic image RGB color model. The investigation aims to determine seed segment zone extraction accuracy, which is obtained on the basis of the presented above colour end texture models Procedures The procedure, which we use to extract the germinated seed image from background, as well as to extract the seed, germ and roots zones, includes the following steps: - The operator crops small areas from the background and from seed segment zones. These areas of pixels are used as training sets for determining the color and texture features of the corresponding zone. The R, G and B components of every pixel from each area are extracted. -Using the HSI, XYZ, NTSC, YCbCr model transformations and the equations (1), (), (3) and (4) we calculate the coordinates of the colour end texture models. b) c) d) Fig.1. Distribution of the normalized pixel vectors in the feature vector space: a) RGB color model; b) HSI color model; c) first texture model; d) third texture model; 1- background; -seed; 3-germ; 4-roots The distribution of the training sets in the feature vector space is presented in Fig. 1. a) Application of interval assessments for homogenous zones extraction The range of each of color or texture coordinates is determined using pixels features from the cropped area. The average values of the ranges are calculated too ISBN: ISSN:

3 (Fig.). These ranges can be changed (increased or decreased) by the operator. The obtained ranges are used as a predicate for homogeneity for zone extraction. The goal is to form binary images of the germinated seed segments. Using binary images the segment geometrical features can be easily determined. D=50% min max D=100% D=150% Fig.. Forming of the coordinate ranges for zone extraction Application of neural networks for homogenous zones extraction A more effective solution of the task for homogenous zones extraction is based on the application of neural networks. For this purpose are used BPN (Backpropagation Network), standard RBFN (Radial Basis Function Network) and a modified RBFN a) b) Fig.3. Neural networks used for homogenous zones extraction: a) BPN; b) standard RBFN; c) modified RBFN networks. The network architectures are presented in Fig. 3. The LVQ (Learning Vector Quantization) network is applied to determine the average values of the coordinate ranges related to the cropped areas. We set the weights of radial neurons in the RBFN using these values. The possibility to classify image pixels into four homogenous zones (background, seed, germ and roots) using BPN, standard RBFN and a modified RBFN is analyzed. The pixels from the cropped areas represent the training set for BPN training. The standard RBFN defines spherical class areas because of the bias of each radial neuron is the same for all input terminals. Really the ranges of variation of pixel features are sufficiently different for each model coordinate. This determines a class shape, which is different from the sphere. That s why we developed a new modified RBFN architecture (Fig.3c), which takes into account this special class feature. The modified RBFN consists of two layers. The first layer includes n x m radial neurons which are distributed into n sub layers (n is the number of coordinates of color/texture model). The number of neurons in one sub layer is m (m is the number of homogenous zones). The radial neurons in the first layer have only one input, corresponding to one of the coordinates of color/texture model. The weights of neurons in each sub layer are equal to the average values of the coordinate ranges of the related homogenous zone. The biases of radial neurons correspond to the coordinate ranges length. This radial layer architecture gives a possibility to form classes, which dimensions are different along the different coordinate axes and correspond to the real dimension of the areas, formed by training sets. The second layer of modified RBFN consists of m radial neurons. Each of them has n inputs, connected to the outputs of the respective radial neuron in the first network layer. The weights of all radial neurons are equal (1, 1, 1). The neuron outputs present the deviation of the input vector from the centers of non spherical classes. To determine the belonging of each input vector to the corresponding class (homogenous zone) we define the output, which has maximum value. c) 3. Seed segment zones extraction Initially we extract the background zone (Fig. 4b) because of the color characteristics of this zone are comparatively homogenous. After that the seed zone is extracted. Normally after this procedure there are several local areas in the frame of each seed segment zone (Fig. ISBN: ISSN:

4 4c). Using morphological transformations like dilation and erosion with a structuring element (which dimensions are smaller then the seed zone Using the obtained seed segments length we can recognize the following seed classes: normal germinated seeds which have a well-built germ (the germ length is more than 1,5 times bigger than the seed length) and well-built roots (the roots number is more than the threshold number and the roots length is more than 1,5 times bigger than the seed length); abnormal germinated seeds which have a germ and roots, which don t respond to these conditions; non geminated seeds, which have not a germ and roots. a) b) c) d) Fig. 4. Segmentation of the germinated seed e) dimensions) we form a compact seed zone. This zone has dimensions which are different from the dimensions of original seed zone. There is not need to restore the original seed shape, but it is needed to define the seed length. It can be determined from the initial seed image. To extract the other segment zones (germ and roots zones) it is enough to define a round area (Fig. 4d), which has a mass center coinciding with the seed mass center. Its diameter is a little bigger than the length of the original seed zone. The germ and roots zones can be extracted after the subtraction of the two areas. To identify the germ zone it is enough to define in which of the extracted zones gets into the zone presented in Fig. 4e. The remaining image zones are the roots zones. 3.3 Determination of seed segment zone characteristics and seed classification After the germinated seed segmentation we define the lengths of the seed segments. A procedure based on segment zone skeletonization is used for this purpose. 3.4 Analysis of the results After the implementation of the described above procedures some of the extracted pixels get into the actual segment zones, some of them are out of these zones. There are different ways to evaluate the procedures accuracy. At first, we can calculate the ratio e 1 of the extracted segment zone area, which gets into the real segment zone (S i ) and the actual segment zone area (S seg ). This comparative indicator shows what part of the real segment zone is determined. Second, as a criterion could be used the ratio e of the extracted segment zone area, which gets out of the real segment zone (S o ) and the actual segment zone area (S seg ). Criterion e shows what part of the real segment zone is incorrectly determined. A more complex criterion can be calculated using the equation (Si - So ) e = *100% (5) Sseg The ratio e gives a common assessment of the accuracy of the segment zone extraction. When the extracted segment zone area S i approaches the real segment zone area S seg, the ratio e increases and approaches value 100%. On the other hand, when the area S i decreases and the area S o increases, the value of the criterion e decreases. The variation of the criterion e when we extract the homogenous zones on the basis of the described in section 3.1 color and texture models and procedures is presented in Fig. 5. The results are obtained using 10 images of typical germinated seeds. The presented results show, that the accuracy of homogenous zones extraction depends on the used color or texture models. The background zone is best extracted because of homogeneity of pixels in this zone. When we use the modified RBFN and the first and third texture models, the accuracy of the background zone extraction is 94.8%. The seed, germ and roots zone extraction accuracy is respectively 74.3%, 4% and 35% when we used modified RBFN and texture models. The proposed texture models increase the homogenous zone extraction accuracy. For example, in comparison with standard RGB color model, the first ISBN: ISSN:

5 texture model increases the accuracy of the seed, germ and roots zones extraction respectively with 6.9%, 1.3 and 34.6%. The tools for pixel classification into the four segment zones have significant influence on the homogenous zones extraction accuracy. The proposed modified RBFN gives the best results in the extraction of the zones including pixels with comparatively variable characteristics (roots, seed and germ). In comparison with the standard RBFN it increases the accuracy of seed and germ zones extraction on the basis of the first texture model with 16.6% and 3.6% respectively. The background zone extraction accuracy obtained by the two neural networks is approximately equal (94.8% and 95%). The modified RBFN gives better results than the standard RBFN because it defines classes with dimensions along the different coordinate axes which correspond to the real dimension of the areas, formed by training sets. The standard RBFN defines spherical class areas. Really the ranges of variation of pixel features are sufficiently different for each model coordinate...the modified RBFN has a sufficient influence on the accuracy improvement especially for the texture models. For instance, the accuracy of roots zone extraction obtained by the first, third and fourth texture model is respectively 35%, 34.6% and 9.9%. When we use any of the standard color models the bigger part of the Germ c) Roots d) Background a) Seed b) Fig.5 Accuracy of the homogenous zones extraction for: a) background; b) seed; c) germ; d) roots; 1-RGB model ; - third texture model; 3-HSV model; 4-fourth texture model; 5- NTSC model; 6-Ycbcr model; 7-XYZ model; 8- first texture model; extracted pixels get out of the real segment zone which determines the negative values of the criterion e. 4 Conclusion The obtained results can be generalized as follows. Because of the fact that the pixel color features of seed segment zones vary in wide ranges, the extracted homogenous zones don t coincide with real segment zones. To extract actual seed segment zones and to determine their geometrical characteristics have to be implemented additional procedures. The accuracy of homogenous zones extraction depends on the used color or texture models. When we use the modified RBFN and the first and third texture models, the accuracy of the background zone extraction is 94.8%. The seed, germ and roots zone extraction accuracy is respectively 74.3%, 4% and 35%. The proposed texture models increase the homogenous zone extraction accuracy. For example, in comparison with standard RGB color model, the first texture model ISBN: ISSN:

6 increases the accuracy of the seed, germ and roots zones extraction respectively with 6.9%, 1.3 and 34.6%. The tool for pixel classification is an important factor for homogenous zones extraction accuracy. The proposed modified RBFN gives the best results in the extraction of the zones including pixels with comparatively variable characteristics (roots, seed and germ). In comparison with the standard RBFN it increases the accuracy of seed and germ zones extraction on the basis of the first texture model with 16.6% and 3.6% respectively. The background zone extraction accuracy obtained by the two neural networks is approximately equal (94.8% and 95%). The modified RBFN has a sufficient influence on the accuracy improvement, especially for the texture models. For instance, the accuracy of roots zone extraction obtained by the first, third and fourth texture model is respectively 35%, 34.6% and 9.9%. When we use any of the standard color models the bigger part of the extracted pixels get out of the real segment zone which determines the negative values of the criterion e. References: [1] Aitkenhead, M.J., I.A. Dalgetty, C.E. Mullins. Weed and crop discrimination using image analysis and artificial intelligence methods, Computers and Electronics in Agriculture, No 39, 003, pp [] Jayas, D. S., J. Paliwal, N. S. Visen. Multy-layer Neural Networks for Image Analysis of Agricultural Products. Journal of Agr. Engineering Research, Vol.77, No, 000, pp [3] Liao, K., M.R. Paulsen, J.F. Reid, B.C. Ni, Bonifacio, E.P. Maghirang. Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of the ASAE, Vol. 36, No 6, 1993, pp [4] Liu, J., M.R. Paulsen. Corn whiteness measurement and classification using machine vision. Transactions of the ASAE, Vol.43, No 3, 000, pp [5] Luo, X., D.S. Jayas, S. Symons. Comparison of statistical and neural network methods for classifying cereal grains using machine vision. Transactions of the ASAE, Vol. 4, No, 1999, pp [6] Majumdar, S., D.S. Jayas. Classification of cereal grains using machine vision: Morphology models I, Transaction of ASAE, Vol. 43, No 6, 000, pp [7] Majumdar, S., D.S. Jayas. Classification of cereal grains using machine vision: Color Models II, Transaction of ASAE, Vol. 43, No 6, 000, pp [8] Majumdar, S., D.S. Jayas. Classification of cereal grains using machine vision, Part3: Texture models. Transactions of the ASAE, Vol. 43, No 6, 000, pp [9] Marchant, J.A., C.M. Onyango. Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination, Computers and Electronics in Agriculture, No , pp. 3-. [10] Mladenov, М., М. Dejanov. Analysis of the Possibilities for Separation of Seed Images on the Basis of Colour and Texture Features, Proceedings of International Conference EE&AE 004, Rousse, Bulgaria, 004, pp [11] Mladenov, М., М. Dejanov. Seed quality assessment using artificial neural networks. Information Technologies and Control, No 3, 007, in press. [1] Mladenov, M., S. Penchev S., B. Borisov, K. Arvanitis, N. Sigrimis. Evaluation of some properties for purity and germination assessment of seeds using computer vision system. Proc. of AgEng 004, Leuven, Belgium, 004. [13] Mladenov, M., S. Penchev. Seeds Purity Assessment Using D Image Analysis. 4 th International Conference on Technology and Automation, Thessaloniki, Greece, 00, pp [14] Ong, S.H., N.C. Yeo, K.H. Lee. Segmentation of color images using a two stage self organizing network. Image and Vision Computing, 00, pp [15] Paliwal, J., N.S. Visen, D.S. Jayas. Evaluation of Neural Network Architectures for cereal grain classification using morphological features, Journal of Agr. Engineering Research, Vol. 79, No 4, 00, pp [16] Paliwali, J., N.S. Visen, D.S. Jayas. Comparison of a neural network and non-parametric classifier for grain kernel identification, Biosystems Engineering, Vol. 85, No 4, 003, pp [17] Paliwal, J., N.S. Visen, D.S. Jayas, N.D. White. Cereal grain and dockage identification using machine vision, Biosystems Engineering, Vol. 85, No 1, 003, pp [18] Sena, D.G., F.A. Pinto. Fall Armyworm Damaged Maize Plant Identification using Digital Images, Biosystems Engineering, Vol. 85, No 4, 003, pp [19] Urena, R., F. Rodriguez, M. Berenguel. A machine vision system for seeds germination quality evaluation using fuzzy logic. Computers and Electronics in Agriculture, No 3, 001, pp ISBN: ISSN: