شناسايي گونه‌هاي گياهي با استفاده از تصاوير برگ بر پايه ويژگي‌هاي بافت و شبكه عصبي

On the use of Textural Features and Neural Networks for Leaf Recognition

برگ گياهان منبع اطلاعاتي مهمي براي پژوهش و شناسايي گياهان هستند. استخراج اين اطلاعات به‌طورعمومي توسط كارشناسان خبره كشاورزي انجام مي­‌گيرد. از آنجا كه برگ­ها ويژگي­‌هاي مناسبي را براي تشخيص انواع گونه‌­هاي گياهي در سامانه‌هاي هوشمند فراهم مي‌كنند، لذا استفاده از سامانه‌هاي هوشمند مي‌­تواند به تشخيص خودكار گونه‌­هاي گياهي كمك كند. اين مقاله روش جديدي را براي شناسايي برگ‌­هاي گونه‌­هاي گياهي با استفاده از الگوريتم استخراج ويژگي بافت GIST ارائه مي‌­دهد كه يك روش استخراج ويژگي عمومي براي طبقه‌بندي تصاوير است. اين روش داراي دقت خوبي در تعيين شباهت­‌ها بين اشياي يكسان در تصاوير مختلف است. در مرحله طبقه‌­بندي داده‌­ها نيز، از شبكه عصبي Patternnet كه براي استخراج الگو مناسب است استفاده مي‌شود. براي ارزيابي روش پيشنهادي، الگوريتم حاصل بر روي داده‌­هاي دو پايگاه داده معتبر كه تنوع گياهي زيادي دارند، اعمال شده است. مقايسه نتايج با الگوريتم‌­هاي متداول استخراج ويژگي از تصاوير برگ‌­ها نشان مي‌­دهد كه الگوريتم GIST علاوه ‌بر سرعت مناسب، داراي دقت طبقه­‌بندي قابل قبولي به‌ويژه در تصاوير هم‌راستا و حتي تصاوير از نوع شبه‌پويش است.

for recognizing various types of plants, so automatic image recognition algorithms can extract to classify plant species and apply these features. Fast and accurate recognition of plants can have a significant impact on biodiversity management and increasing the effectiveness of the studies in this regard. These automatic methods have involved the development of recognition techniques and digital image processing pattern. Most of the previous studies on the classification and identification of plant species from leaf images are based on the shape, texture and color features. There were also different methods of data modeling which have been used to leave plant recognition. In this paper, we investigate a novel approach for the recognition of plant species using texture feature GIST to extract general features. In the classification step, Patternnet feed forward neural network algorithm has been applied. Essentially, the GIST feature has been designed to be employed for image classification. In this study, GIST feature vectors are considered as the basis of the leaves’ classification. The GIST descriptor of an image is computed by the first filtering of an image by a filter bank of Gabor filters, and then averaging the responses of filters in each block on a no overlapping grid. For evaluation of our approach, we have applied the algorithm on scan and pseudo-scan images of two famous different datasets Image CLEF2012 and Leaf snap with a high various. The results show that in comparison to some widely used algorithms, our approach outperforms in the case of time and also the accuracy of classification. Substantial results can be achieved when the image of the plants are aligned with one another and when we deal with pseudo scan images. The detection of combinations of leaves that have jagged edges is an important contribution of this study. In many of the previous algorithms, the computational complexity of this detection is high. While by using the GIST feature vector, these types of images are processed simply and precisely (above 90%). precisely (above 90%).