Fruit Ripeness

Fruit ripeness assessment is crucial for optimizing harvest timing and ensuring product quality, traditionally relying on labor-intensive manual methods. Current research focuses on automating this process using computer vision and machine learning, employing techniques like convolutional neural networks (CNNs), including DenseNets and YOLO object detection, and incorporating hyperspectral imaging to capture subtle ripeness cues invisible to the naked eye. These advancements leverage both visible and near-infrared spectral data to predict ripeness indicators such as sugar content and firmness, improving efficiency and reducing human error in agricultural practices. The resulting technologies promise significant improvements in yield estimation, quality control, and automation of harvesting processes.

Papers

January 9, 2024

Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment
Chollette C. Olisah, Ben Trewhella, Bo Li, Melvyn L. Smith, Benjamin Winstone, E. Charles Whitfield, Felicidad Fernández Fernández, Harriet Duncalfe
Convolutional Neural Network Hyperspectral Image Tomato Plant Berry Picking Uncontrolled Environment Fruit Ripeness

November 18, 2023

An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection
Boyang Deng, Xin Wen, Zhan Gao
Neural Network CNN Network Fruit Ripeness

March 15, 2023

Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Yue Pan, Federico Magistri, Thomas Läbe, Elias Marks, Claus Smitt, Chris McCool, Jens Behley, Cyrill Stachniss
Pose Estimation Robotic Harvesting Fruit Ripeness Panoptic Mapping

February 13, 2023

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection
Ritayu Nagpal, Sam Long, Shahid Jahagirdar, Weiwei Liu, Scott Fazackerley, Ramon Lawrence, Amritpal Singh
Deep Learning Application Proficiency Color Object YOLO Object Fruit Ripeness

February 6, 2023

Tendon-Driven Soft Robotic Gripper with Integrated Ripeness Sensing for Blackberry Harvesting
Alex Qiu, Claire Young, Anthony Gunderman, Milad Azizkhani, Yue Chen, Ai-Ping Hu
Soft Robotics Tomato Plant Fruit Ripeness Soft Robotic Gripper Selective Chrysanthemum Harvesting Robot

December 29, 2022

Fruit Ripeness Classification: a Survey
Matteo Rizzo, Matteo Marcuzzo, Alessandro Zangari, Andrea Gasparetto, Andrea Albarelli
Deep Learning Machine Learning Timely Survey FRUIT Produce SMOOTHIE Fruit Ripeness

December 8, 2022

Fruit Quality Assessment with Densely Connected Convolutional Neural Network
Md. Samin Morshed, Sabbir Ahmed, Tasnim Ahmed, Muhammad Usama Islam, A. B. M. Ashikur Rahman
Neural Network SE SPP DenseNet Tomato Plant Fruit Ripeness Recognition Accuracy

July 10, 2022

Facilitated machine learning for image-based fruit quality assessment
Manuel Knott, Fernando Perez-Cruz, Thijs Defraeye
Convolutional Neural Network Pre Trained Vision Transformer Fruit Ripeness Apple Detection

April 26, 2022

Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning
Cecilia Occhiuzzi, Francesca Camera, Michele D'Orazio, Nicola D'Uva, Sara Amendola, Giulio Maria Bianco, Carolina Miozzi, Luigi Garavaglia, Eugenio Martinelli, Gaetano Marrocco
Machine Learning Support Vector Machine ROOM Layout FRUIT Produce SMOOTHIE Fruit Ripeness

March 10, 2022

Hyperspectral Imaging for cherry tomato
Yun Xiang, Qijun Chen, Zhongjin Su, Lu Zhang, Zuohui Chen, Guozhi Zhou, Zhuping Yao, Qi Xuan, Yuan Cheng
Hyperspectral Imaging Tomato Plant Fruit Ripeness

Fruit Ripeness

Papers

Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment

An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection

Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Tendon-Driven Soft Robotic Gripper with Integrated Ripeness Sensing for Blackberry Harvesting

Fruit Ripeness Classification: a Survey

Fruit Quality Assessment with Densely Connected Convolutional Neural Network

Facilitated machine learning for image-based fruit quality assessment

Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning

Hyperspectral Imaging for cherry tomato