# DeepFruits: A Fruit Detection System Using Deep Neural Networks ## Prior works ### Origin paper #### DeepFruits: A Fruit Detection System Using Deep Neural Networks Inkyu Sa, ZongYuan Ge, Feras Dayoub, B. Upcroft, Tristan Perez, C. McCool 2016 #### Automated Bell Pepper Harvesting using Robotic Vision System Silpa Ajith Kumar, J. S. Kumar 2019 #### Automatic Fruits Classification System Based on Deep Neural Network Khadija Munir, A. I. Umar, Waqas Yousaf 2020 #### Convolutional Neural Networks (CNN) for Detecting Fruit Information Using Machine Learning Techniques Fouzia Risdin, P. Mondal, Kazi Mahmudul Hassan 2020 #### Deep fruit detection in orchards Suchet Bargoti, J. Underwood 2016 #### DetSSeg: A Selective On-Field Pomegranate Segmentation Approach Shubham S. Mane, Prashant Bartakke, Tulshidas Bastewad 2023 #### Visual detection of occluded crop: For automated harvesting C. McCool, Inkyu Sa, Feras Dayoub, Christopher F. Lehnert, Tristan Perez, B. Upcroft 2016 #### Fruit Quantity and Ripeness Estimation Using a Robotic Vision System Michael Halstead, C. McCool, Simon Denman, Tristan Perez, C. Fookes 2018 #### Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards Suchet Bargoti, J. Underwood 2016 #### Deep Count: Fruit Counting Based on Deep Simulated Learning Maryam Rahnemoonfar, Clay Sheppard 2017 #### Building Efficient Fruit Detection Model Pavan N. Kunchur, V. Pandurangi, Madhu Hollikeri 2019 #### Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry Madeleine Stein, Suchet Bargoti, J. Underwood 2016 #### Multiple Pedestrian Detection Depending on Faster Region-based Convolutional Neural Network (RCNN) Ghalia Shariha, M. Elmogy, Eman M. El-Daydamony, A. Atwan 2019 #### Fruit Quantity and Quality Estimation using a Robotic Vision System Michael Halstead, C. McCool, Simon Denman, Tristan Perez, C. Fookes 2018 #### Sensors and systems for fruit detection and localization: A review Aleana Gongal, Suraj Amatya, M. Karkee, Qin Zhang, Karen Lewis 2015 #### A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field P. Lin, W. S. Lee, Y. M. Chen, N. Peres, C. Fraisse 2019 #### Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’ A. Koirala, K. Walsh, Zhenglin Wang, C. McCarthy 2019 #### On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture Inkyu Sa, C. McCool, Christopher F. Lehnert, Tristan Perez 2015 #### Deployment of Deep Learning Models on Resource-Deficient Devices for Object Detection Ameema Zainab, Dabeeruddin Syed 2020 #### Counting Apples and Oranges With Deep Learning: A Data-Driven Approach Steven W. Chen, Shreyas S. Shivakumar, Sandeep Dcunha, J. Das, Edidiong Okon, Chao Qu, C. J. Taylor, Vijay R. 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Li, Zi-ze Liang 2019 ## How to read the graph Each node is an academic paper related to the origin paper. - Papers are arranged according to their similarity (this is not a citation tree) - Node size is the number of citations - Node color is the publishing year - **Similar** papers have strong connecting lines and cluster together ## Paper Details [DeepFruits: A Fruit Detection System Using Deep Neural Networks](https://www.semanticscholar.org/paper/9397e7acd062245d37350f5c05faf56e9cfae0d6) Inkyu Sa + 4 authors C. McCool 2016, Italian National Conference on Sensors This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.