Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance

The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

[EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero García, S.; Lee, WS.; Aleixos Borrás, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). 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Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51, 220–226.Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.Barreiro, P., Zheng, C., Sun, D.-W., Hernández-Sánchez, N., Pérez-Sánchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47, 189–198.Basavaprasad, B., & Ravi, M. (2014). A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology, 3, 310–315.Birth, G. S. (1976). How light interacts with foods. In: Gafney J.Jr.(Ed.), Quality detection in foods (pp. 6–11). St. Joseph: ASAE.Blanc, P.G.R., Blasco, J., Moltó, E., Gómez-Sanchis, J., & Cubero, S. (2010) System for the automatic selective separation of rotten citrus fruits. Patent number EP2133157 A1 CN101678405A, EP2133157A4, EP2133157B1, US20100121484Blasco, J., Aleixos, N., & Moltó, E. (2007a). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81(3), 535–543.Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007b). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damages in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145.Blasco, J., Cubero, S., & Moltó, E. (2016). Quality evaluation of citrus fruits. In D.-W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed.). San Diego: Academic Press.Bulanon, D. 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International Journal of Agricultural and Biological Engineering, 7, 20–27.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., Kaichi, T., & Okamoto, S. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4, 7–11.Kondo, N., Kuramoto, M., Shimizu, H., Ogawa, Y., Kurita, M., Nishizu, T., Chong, V. K., & Yamamoto, K. (2009). Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Engineering in Agriculture, Environment and Food, 2, 54–59.Kurita, M., Kondo, N., Shimizu, H., Ling, P. P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21, 533–540.Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.Ladaniya, M. S. (2010). Citrus fruit: biology, technology and evaluation. San Diego: Academic Press.Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture. doi: 10.1007/s11119-016-9443-z.Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78, 38–48.Li, J., Rao, X., & Ying, Y. (2012a). Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. Journal of the Science of Food and Agriculture, 92, 125–134.Li, J., Rao, X., Wang, F., Wu, W., & Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69.Li, J., Rao, X., Ying, Y., & Wang, D. (2010). Detection of navel oranges canker based on hyperspectral imaging technology. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 26, 222–228.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., & Mangan, R. (2012b). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32–46.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). 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Autonomous Fruit Harvester with Machine Vision

This study presents an autonomous fruit harvester with a machine vision capable of detecting and picking or cutting an orange fruit from a tree. The system of is composed of a six-degrees of freedom (6-DOF) robotic arm mounted on a four-wheeled electric kart. The kart uses ZED stereo camera for depth estimation of a target. It can also be used to detect trees using the green detection algorithm. Image processing is done using Microsoft Visual Studio and OpenCV library. The x & y coordinates and distance of the tree are passed on to Arduino microcontroller as inputs to motor control of the wheels. When the kart is less than 65cm to the tree, the kart stops and the robotic arm system takes over to search and harvest orange fruits. The robotic arm has a webcam and ultrasonic sensor attached at its end-effector. The webcam is used for orange fruit detection while ultrasonic sensor is used to provide feedback on the distance of the orange fruit to end-effector. Multiple fruit harvesting is successfully done. The success rate of harvesting and putting fruit into the basket is 80% and 85% for the gripper end-effector and cutter end-effector respectively

Applications of Image Processing in Viticulture: A Review

The production of high quality grapes for wine making is challenging. Significant progress has been made in the automated prediction of harvest yields from images but the analysis of images to predict the quality of the harvest has yet to be fully addressed. The quality of wine produced depends in part on the quality of the grapes harvested and therefore on the presence of disease in the vineyard. There is potential for automated early detection of disease in grape crops through the development of accurate techniques for image processing. This paper presents a review of current research and highlights some of the key challenges for geo-computation (image processing, computer vision and data mining techniques) to inform the management of vineyards and highlights the key challenges for in-field image capture and analysis. An exploration of potential applications for the knowledge generated by imaging techniques is then presented. This discussion is driven by the current interest in the effect of rapid and dramatic climate change on the production of wine and focuses on how this information might be utilized to inform the design and validation of accurate predictive models