https://doi.org/10.4081/jae.2023.1545
Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence
Published: 30 October 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, and costly, and its accuracy depends on the scale of the sample. To overcome these problems, hybrid approaches of computer vision, deep learning (DL), and machine learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally, a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy up to 94.81% and yield prediction accuracy up to 99.58%.
Abdul Hakkim V.M., Abhilash Joseph E., Ajay Gokul A.J., Mufeedha K. 2016. Precision farming: The future of Indian agriculture. J. Appl. Biol. Biotechnol. 4:68-72.
DOI: https://doi.org/10.7324/JABB.2016.40609
Altaheri H., Alsulaiman M., Muhammad G. 2019. Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access. 7:117115-33.
DOI: https://doi.org/10.1109/ACCESS.2019.2936536
Arad B., Kurtser P., Barnea E., Ha B., Edan Y., Ben-Shahar O. 2019. Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting. Sensors. 19:1390.
DOI: https://doi.org/10.3390/s19061390
Badeka E., Kalabokas T., Tziridis K., Nicolaou A., Vrochidou E., Mavridou E., Papakostas G.A., Pachidis T. 2019. Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors. Springer Link Computer Vision Systems. 98-109.
DOI: https://doi.org/10.1007/978-3-030-34995-0_9
Baeten J., Donn K., Boedrij S., Beckers W. 2008. Autonomous fruit picking machine: A robotic apple harvester. Field and Service Robotics. 42:531-9.
DOI: https://doi.org/10.1007/978-3-540-75404-6_51
Barbole D.K., Jadhav P.M. 2021. Comparative Analysis of Deep Learning Architectures for Grape Cluster Instance Segmentation. Inf. Technol. Industry. 9.
DOI: https://doi.org/10.17762/itii.v9i1.138
Barbole D.K., Jadhav P.M. 2022. Grape Yield Prediction using Deep Learning Regression Model. 2022 International Conference for Advancement in Technology (ICONAT), Goa, India, 1-6.
DOI: https://doi.org/10.1109/ICONAT53423.2022.9726026
Barbole D.K., Jadhav P.M. 2023a. GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets. Mendeley Data.
Barbole D.K., Jadhav P.M. 2023b. GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications. Data Brief. 48:109100.
DOI: https://doi.org/10.1016/j.dib.2023.109100
Barbole D., Jadhav P., Patil S. 2021. A Review on Fruit Detection and Segmentation Techniques in Agricultural Field. International Conference on Image Processing and Capsule Networks. 300:269-88.
DOI: https://doi.org/10.1007/978-3-030-84760-9_24
California Historical Society Collection. 2012. Close-up of a grape cluster on a vine. University of Southern California (USC) Digital Library.
Cecotti H., Rivera A., Farhadloo M., Pedroza M.A. 2020. Grape detection with convolutional neural networks. Expert Syst. Appl. 159.
DOI: https://doi.org/10.1016/j.eswa.2020.113588
Ceres R., Pons J.L., Jiménez A.R., Martín J.M. 1998. Agribot: A robot for aided fruit harvesting. Industrial Robot. 5.
Chen S., Shivakumar S., Dcunha S., Das J., Okon E., Qu C., Kumar V. 2017. Counting apples and oranges with deep learning: A data-driven approach. IEEE Robot. Autom. Lett. 2:781-8.
DOI: https://doi.org/10.1109/LRA.2017.2651944
Fernandez-Maloigne C., Laugier D., Boscolo C. 1993. Detection of apples with texture analyses for an apple picker robot. Proceedings of the Intelligent Vehicles ‘93 Symposium, IEEE Xplore. pp. 323-8.
FAO. 2009. Global agriculture towards 2050. High-level Expert Forum. Available at the link: https://www.fao.org/wsfs/forum2050/wsfs-forum/en/
Grasso G., Recce M. 1996. Scene analysis for an orange picking robot. Proceeding in International Conference of Computer Technology in Agriculture (ICCTA ‘96).
Habaragamuwa H., Ogawa Y., Suzuki T., Shiigi T., Ono M., Kondo N. 2018. Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network. Eng. Agric. Environ. Food. 11:127-38.
DOI: https://doi.org/10.1016/j.eaef.2018.03.001
Kang H., Chen C. 2019a. Fruit Detection, Segmentation and 3D Visualization of Environment in Apple Orchards. Comput. Electron. Agric. 171:105302.
DOI: https://doi.org/10.1016/j.compag.2020.105302
Kang H., Chen C. 2019b. Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards. Sensors. 19:4599.
DOI: https://doi.org/10.3390/s19204599
Khan N., Fahad S., Naushad M., Faisal S. 2020. Grape Production Critical Review in the World. SSRN Electron. J.
DOI: https://doi.org/10.2139/ssrn.3595842
Lee J., Nazki H., Baek J., Hong Y., Lee, M. 2020. Artificial Intelligence Approach for Tomato Detection and Mass estimation in Precision Agriculture. Sustainability 12:9138.
DOI: https://doi.org/10.3390/su12219138
Liu S., Whitty M. 2015. Automatic grape bunch detection in vineyards with an SVM classifier. J. Appl. Log. 13:643-53.
DOI: https://doi.org/10.1016/j.jal.2015.06.001
Liu X., Chen S.W., Aditya S., Sivakumar N., Dcunha S., Qu C., Taylor C.J., Das J., Kumar V. 2018. Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
DOI: https://doi.org/10.1109/IROS.2018.8594239
Liu Z., Wu J., Fu L., Maje Y., Feng Y., Li R., Cui Y. 2020. Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion. IEEE Access. 8.
DOI: https://doi.org/10.1109/ACCESS.2019.2962513
Luo L., Tang Y., Lu Q., Chen X., Zhang P., Zou X. 2018. A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard. Comput. Ind. 99:130-9.
DOI: https://doi.org/10.1016/j.compind.2018.03.017
Luo L., Tang Y., Zou X., Wang C., Zhang P., Feng W. 2016. Robust Grape Cluster Detection in a Vineyard by Combining the Ada- Boost Framework and Multiple Color Components. Sensors. 16.
DOI: https://doi.org/10.3390/s16122098
Marani R., Milella A., Petitti A., Reina G. 2020. Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera. J. Prec. Agric. 22:387-413.
DOI: https://doi.org/10.1007/s11119-020-09736-0
Ni X., Li C., Jiang H., Takeda F. 2020. Deep learning image segmentation and extraction of blueberry fruit traits associated with harvest ability and yield. Horticul. Res. 7.
DOI: https://doi.org/10.1038/s41438-020-0323-3
Nuske S., Achar S., Bates T., Narasimhan S. 2011. Yield estimation in vineyards by visual grape detection. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
DOI: https://doi.org/10.1109/IROS.2011.6048830
Ranganathan J., Waite R., Searchinger T., Hanson C. 2018. How to Sustainably Feed 10 Billion People by 2050. World Resources Institute. Available from: https://www.wri.org/insights/howsustainably-feed-10-billion-people-2050-21-charts
Santos T.T., De-Souza L.L., Dos-Santos A., Avila S. 2020. Grape detection, segmentation and tracking using deep neural networks and three-dimensional association. Comput. Electron. Agric. 170.
DOI: https://doi.org/10.1016/j.compag.2020.105247
Stein M., Bargoti S., Underwood J. 2019. Image Based Mango Fruit Detection, Localization and Yield Estimation using Multiple View Geometry. Sensors. 16:1915
DOI: https://doi.org/10.3390/s16111915
Tang Y., Chen M., Wang C., Luo L., Li J., Lian G., Zou X. 2020. Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review. Frontiers Plant Sci. 11.
DOI: https://doi.org/10.3389/fpls.2020.00510
Tang Y., Zhou H., Wang H., Zhang Y. 2023. Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision. Exp. Syst. Appl. 211.
DOI: https://doi.org/10.1016/j.eswa.2022.118573
Wang C., Tang Y., Zou X., SiTu W., Feng W. 2017. A Robust Fruit Image Segmentation Algorithm against Varying Illumination for Vision System of Fruit Harvesting Robot. Optik - Int. J. Light Electron. Opt. 131:626-31.
DOI: https://doi.org/10.1016/j.ijleo.2016.11.177
Zhang C., Ding H., Shi Q., Wang Y. 2022. Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network. Agriculture. 12.
DOI: https://doi.org/10.3390/agriculture12081242
Zhou Y., Tang Y., Zou X., Wu M., Tang W., Meng F., Zhang Y., Kang H. 2022. Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm. Appl. Sci. 12:12959.
DOI: https://doi.org/10.3390/app122412959
How to Cite
Barbole, D. and Jadhav, P. M. (2023) “Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence”, Journal of Agricultural Engineering, 55(1). doi: 10.4081/jae.2023.1545.
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