Image Classification of Forage Plants in Fabaceae Family Using Scale Invariant Feature Transform Method

Abstract

This paper proposes a novel method for the image classification of forage plants in fabaceae family by using Scale Invariant Feature Transform (SIFT) method. The color image extension jpeg color mode RGB adjust the image to 1000x1000 pixels to get a single image of the template file. All of the sample images, four prototype images were standard scaled and rotated. The image was obtained through the image extraction process using SIFT implements and matching dataset of Forage Plants leaves with matching points to evaluate the accuracy of flea leaf identification, it was found that Senna siamea, Clitoria ternatea and Pithecellobium dulce leaves 100% accuracy but Sesbania grandiflora Desv was obtained with 0% accuracy. The total accuracy of all 4 plants 75%, indicated that the photosynthesis of SIFT leaves was suitable for Senna siamea, Clitoria ternatea and Pithecellobium dulce Because it is 100% accurate, but not with Sesbania grandiflora Desv leaves. The accuracy is 0% because the leaves are dark green. The leaves are not clear. And the leaves are slender, evenly spaced leaves, which makes it a very rare feature. While Senna siamea, Clitoria ternatea and Pithecellobium dulce leaves are clear. Leaf edge is unique. Include appropriate techniques for recognition and classification.