Harvestable Black Pepper Recognition Using Computer Vision

The objective of the research presented in this paper is to speed up recognition of harvestable black pepper using computer vision for automated black pepper harvesting. In this paper we introduce a novel dataset of black pepper images acquired using a digital camera. The proposed system is based on a combination of several image processing techniques and a deep learning model to achieve a system capable of recognizing and detecting harvestable black pepper from different elements of the scene, such as leaves, tree trunks branches and unripe pepper. The system is composed of a 3-stage image processing and a verification model in order to achieve 100% accuracy. This approach not only increase the accuracy but also reduce the processing time and computational resources required as the system moves from one stage to another only if a set of pre-defined conditions are met. After performing trial and error method on a number of different classifiers we decided to use ResNet-50, a CNN based classifier for the final validation of test results due to its immense speed and accuracy. The experimental results are showing promising 100% global accuracy with reasonable scan time which will enable real time application.

[1]  Tristan Perez,et al.  Fruit Quantity and Ripeness Estimation Using a Robotic Vision System , 2018, IEEE Robotics and Automation Letters.

[2]  Min Zuo,et al.  CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture , 2019, Sensors.

[3]  Tristan Perez,et al.  DeepFruits: A Fruit Detection System Using Deep Neural Networks , 2016, Sensors.

[4]  Jaakko Astola,et al.  An Introduction to Nonlinear Image Processing , 1994 .

[5]  Edward J. Delp,et al.  Morphological operations for color image processing , 1999, J. Electronic Imaging.

[6]  Alpha Agape Gopalai,et al.  Robotic vision system design for black pepper harvesting , 2013, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013).

[7]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[8]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[10]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[14]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[15]  Geoffrey Zweig,et al.  Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention , 2016, INTERSPEECH.

[16]  Wolfgang Förstner,et al.  Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images , 2000 .

[17]  Roemi Fernández,et al.  Automatic Detection of Field-Grown Cucumbers for Robotic Harvesting , 2018, IEEE Access.

[18]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[19]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.