• Anala lakshmi veera raga ganani

Abstract- By using traditional methods recognizing herbs will takes a lot of effort and time to identify the desired plant from among the thousands of herbs. Because the pharmacist and botanist do not need to collect plants using traditional methods, identification of herbs via a vision system is advantageous. Here we are proposing Classification and Detection of Herbs Based on Shape and Texture Features Deep Learning and YOLO V2. In the proposed system, we'll use convolutional neural networks to categorize herbs. According to experimental findings, this model is superior to the machine learning technique known as the Support Vector Machine feature classifier. This approach of categorization turns out to be more effective for classifying and detecting herbs. Here we will investigate and build an efficient classifier and detector better than previous that is Deep Learning Neural Network (DLNN) and YOLO V2 Object detector.

Keywords: - Herbs classification, machine learning, feature extraction, support vector machine, deep neural networks, YOLO V2 Object detector, Object Detection.

"Classification and Detection of Herbs Based on Shape and Texture Features using Deep Learning and YOLO V2", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.e51-e60, October-2022, Available :http://www.jetir.org/papers/JETIR2210409.pdf

"Classification and Detection of Herbs Based on Shape and Texture Features using Deep Learning and YOLO V2", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppe51-e60, October-2022, Available at : http://www.jetir.org/papers/JETIR2210409.pdf