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Automatic Recognition of Pulse Crops Using Image Progessing

Muhammad Mustagis Billah
Department of Computer Science and Mathematics, Bangladesh Agricultural University, Mymensingh, Bangladesh

Muhammad Ashik-E-Rabbani
Department of Farm Power and Machineries, Bangladesh Agricultural University, Mymensingh, Bangladesh

Abu Tayeb Muhammad Alimuzzaman
Ministry of Women and Children Affairs, Dhaka, Bangladesh

Abstract/ Executive Summary:

The present study was explored the feasibility of implementing fast and reliable computer-based systems for the automatic re cognition of pulse crops from color and gray intensity images. Pulse crop’s size, shape, color and texture characteristics are obtained by standard image-processing techniques and their discriminating power as classification features was assessed. These investigations were performed on a database containing 102 images of most common four pulse crop’s that were Lentil, Ground Nut, Chick-pea and Split-pea. Each image contains approximately 15-20 pulses of same and mix varieties together and considers the implementation of a simple RGB and gray color model for recognition. The results indicate that classifier based on an adequately selected set of classification features has an excellent performance. The success rates of Lentil, Ground Nut, Chic k-pea and Split-pea were 90.02%, 90.33%, 91.96% and 83.58%, respectively. In addition, the recognition gave highest percentages using distinct characteristics as classification features.

Keywords: Automatic, Recognition, Image processing, Machine vision, Pulse crops

Location: Notun bazaar, Mymensingh; Tajmohol, Jamalpur and Jamalpur bij vandar , Jamalpur.

Start Date:

End Date:

Program Area: Resource Development and Management

Non-commodity: Resource inventory

Objectives:

To analysis pulse crop recognition using image processing system.

Methodology:

The present paper proposes a scheme for automated recognition of 4 classes of pulse category by analyzing shapes, colors and size obtained from a collection of their pulse images, using features based on RGB color model, Gray color model and Neighborhoods & Connectivity approaches with various types of machine vision classifiers. Experimentations are performed by using 102 images containing 808 individual pulses carried out on a Nikon D5100 SLR, 16.2 Megapixels, CMOS sensored digital camera of a high resolution with 24-bit colors. Crops were collected from three seed markets of Notun bazaar, Mymensingh; Tajmohol, Jamalpur and Jamalpur bij vandar , Jamalpur. During image collection, the pulse crop was placed on a white board and the light was good enough from all sides to eliminate the shade. The image was taken from top as bird view and the same height for all samples from crops. So the actual sizes of images were not varied from image to image. 15 to 20 pulse crops were taken in a white board and an image of that pulse crop were taken as JPEG format. All pulse images were in 1024 x 768 pixels. There were no restriction on the direction of pulse when Image taking. Edges correspond in general to important changes in physical or geometrical properties of objects in the scene that attempts to characterize the intensity in the image. Segmentation is used to separate objects within an image from the other objects and as well as from the background. In color based segmentation approach it first removes edges whose color is significantly different from that of the object. Mainly it eliminates foreground edges whose color is different from background color. An edge can be classified as pulse crop edge if the color of any one of its neighbors or itself was close to the color of the pulse crop.RGB format was used to read JPEG images directly in a red-green-blue (RGB) color system where each pixel contain ranging from 0 to 255 of each red, green, and blue in tensities. The values of these three primary colors make up the actual color for each pixel. The 8-neighbor boundary detection system was applied to boundary detection. From the images, the pulse crop areas were identified and isolated individually. The remaining pixels outside the boundary were colored as pure white (255, 255, 255). The total pixels were counted inside the crop boundary area. To simplify the pixels, image were then converted to a one coordinate format based on gray level. Thus, all pixels were given a value of one and those associated with a different colored object were coded as intensity in the range zero to one. This method simplifies images and removes background noise. Firstly pulse crop has detected by its color and it varies from crop to crop. For example the crop named Ground Nut is approximately black in color. The image processing system was comparing the color of Ground Nuts with the previously stored Ground Nuts color index. If the current color matches then it recognized as image of Ground Nuts. The similar color was decided by the RGB and gray scale ranges. Inside a boundary all the pixels were selected from the same seed. The next step was to measure the pulse crops size. It is known that every pure pulse crop has a standard size. The size was measured by counting the number of pixels in the area of the crops image. At the boundary detecting time it was counted the pixel of the crops image. It was counted how many same colored pixels contain inside the image boundary. The final step was to measure the shape of the pulse crop. According to the geometrical formula –“if major axis is equal to minor axis of an ellipse then it is a circle.” Some crops were circular shaped but some are different. At the time of area measurement the major axis, minor axis and the center point of the major axis was calculated. The highest length of the seed was taken as major axis. For each seed the maximum and minimum x and y co-ordinate was calculate. Also from this two x,y co-ordinate a rectangular shaped was formed virtually. On the major axis, axis center point and the minor axis shape of the crop can be easily measured. So, Maximum distance was calculated and width of the crop has measured by maximum distance to perpendicular of the center point as minor axis.

Source of Information: Res. Agric. Livest. Fish. Vol. 2, No. 2, August 2015: 215-220, ISSN : P-2409-0603

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

Research in image processing system to-date remain s centered on technological issues and is mostly application driven. This study was undertaken to develop an image processing system to recognition pulse crops taken from the different places. There were 808 images for processing and the performance of the automated system was compared to manual system. The success rate for the identification rate of recognized pulse crops were observed to be as high as 83.58 to 91.96%, while the success rate for Lentil, Ground Nut, Chick-pea and Split-pea were 90.02%, 90.33%, 91.96% and 83.58% respectively. Experimental result indicates that our algorithm is workable with satisfactory accuracy on 4 kinds of pulse crops. The average recognition ratio was greater than 88%. Compared with other manual methods, this algorithm is fast in execution, efficient in recognition and easy in implementation. By applying image processing system in our agricultural field, we can analysis our pulse crops purity quickly and by following appropriate control measure sustainable agriculture can be achieved. Future work is under consideration to improve the identification of pulse crops to purity measurement at different condition with the help of different algorithms.

Knowledge Management: Journal