AI-Powered Subterranean Crop Harvesting Stage Detection and Automation

Abstract

This work introduces a system that combines deep learning with robotics to automate the detection and harvesting of beetroot crops. The system utilizes a convolutional neural network (CNN) based on the ResNet-50 architecture for image classification and is trained to identify beetroot plants at their ideal harvesting stage. With an accuracy of 99.08% and a precision of 98.39%, the model ensures dependable detection. A robotic platform, equipped with a camera, captures images in the field, which are processed by the ResNet-50 model to assess the readiness of the beetroots. Once a beetroot is confirmed ready for harvest, a robotic arm is triggered to carry out the harvesting operation. This system tackles the difficulty of timely and accurate crop identification, automating a critical aspect of the harvesting process. By leveraging deep learning for detection and robotics for execution, the system aims to minimize manual oversight and improve the effectiveness of beetroot harvesting operations.

Keywords

• Agriculture Automation
• Deep Learning
• CNN
• Harvesting
• Robotics

References

1. [1] Harakannanavar, S. S., Rudagi, J. M., Puranikmath,V.I., Siddiqua, A., Pramodhini, R., “Plant leaf disease detection using computer vision and machine learning algorithms”, Global Transitions Proceedings, 3(1): 305-310, (2022). DOI: https://doi.org/10.1016/j.gltp.2022.03.016
2. [2] Zhu, X., Zhu, M., Ren, H., “Method of plant leaf recognition based on improved deep convolutional neural network”, Cognitive Systems Research, 52: 233-233, (2018). DOI: https://doi.org/10.1016/j.cogsys.2018.06.008
3. [3] Ghazia, M. M., Yanikoglu, B., Aptoula, E., “Plant identification using deep neural networks via optimization of transfer learning parameters”, Neurocomputing, 235: 228-235, (2020). DOI: http://dx.doi.org/10.1016/j.neucom.2017.01.018
4. [4] Islam, Md. M, Adil, Md. A. A., Talukder, Md. A., “DeepCrop: Deep learning-based crop disease prediction with web application”, Journal of Agriculture and Food Research, 14: (2023). DOI: https://doi.org/10.1016/j.jafr.2023.100764
5. [5] Karahan, T., Nabiyev, V., “Plant identification with convolutional neural networks and transfer learning”, Cognitive Systems Research, 27(5):638-645, (2021). DOI: https://doi.org/10.5505/pajes.2020.84042
6. [6] Lee, S, H., Chan, C, S., Mayo, S, J., Remagnino, P., “How deep learning extracts and learns leaf features for plant classification”, Pattern Recognition, 71:1-13, (2017). DOI: http://dx.doi.org/10.1016/j.patcog.2017.05.015
7. [7] Hassan, M. S. K., Maji, A, K., “Identification of Plant Species Using Deep Learning”, Proceedings of International Conference on Frontiers in Computing and Systems, Advances in Intelligent Systems and Computing, 1255: (2021). DOI: https://doi.org/10.1007/978-981-15-7834-2_11
8. [8] Zhu, H., Liu, Q., Qi, Y., Huang, X., Jiang, F., Zhang, S., “Plant identification based on very deep convolutional neural networks”, Multimed Tools Appl, 77: 779-797, (2018). DOI: https://doi.org/10.1007/s11042-017-5578-9

9. [9] Diqi, M., Mulyani, S.H., “Implementation of CNN for Plant Leaf Classification”, International Journal of Informatics and Computation, 2(2): 1-10, (2020). DOI: https://doi.org/10.35842/ijicom.v2i2.28
10. [10] Zamir, M., Ali, N., Naseem, A., Ahmed, F. A., Zafar, B., Assam, M., Othman, M., Attia, E.A., “Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi”, Computation, 10(9):148, (2022). DOI: https://doi.org/10.3390/computation10090148
11. [11] Bansal, P., Kumar, R., Kumar, S., “Disease Detection in Apple Leaves Using Deep Convolutional Neural Network”, Agriculture, 11(7): 617, (2021). DOI: https://doi.org/10.3390/agriculture11070617
12. [12] Sahidan, N, F., Juha, A, K., Norasiah, M., Ibrahim, Z., “Flower and leaf recognition for plant identification using convolutional neural network”, Indonesian Journal of Electrical Engineering and Computer Science, 16(2), (2019). DOI: http://doi.org/10.11591/ijeecs.v16.i2.pp737-743
13. [13] Hu, J., Chen, Z., Yang, M., Zhang, R., Cui, Y., “A Multiscale Fusion Convolutional Neural Network for Plant Leaf Recognition”, IEEE Signal Processing Letters, IEEE, 25(6): 853-857, (2018). DOI: https://doi.org/10.1109/LSP.2018.2809688
14. [14] Xenakis, A., Papastergiou, G., Vassilis, C. G., George, S., “Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis”, 11th International Conference on Information, Intelligence, Systems and Applications, IISA, Piraeus, Greece, (2020). DOI: https://doi.org/10.1109/IISA50023.2020.9284356
15. [15] Lyndon, T. B., Noel, B. L., “Classification of Healthy and Unhealthy Abaca Leaf Using a Convolutional Neural Network (CNN)”, 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, (2021). DOI: https://doi.org/10.1109/10.1109/HNICEM54116.2021.9732050
16. [16] Kanda, P., Xia, K., Sanusi, O. H., “A Deep Learning-Based Recognition Technique for Plant Leaf Classification," IEEE Access, IEEE, (2021). DOI: https://dx.doi.org/10.1109/ACCESS.2021.3131726
17. [17] Bisen, D., “Deep convolutional neural network based plant species recognition through features of leaf”, Multimedia Tools and Applications, (2021). DOI: https://doi.org/10.1007/s11042-020-10038-w
18. [18] V, Singh., Varsha., Misra, A. K., “Detection of unhealthy region of plant leaves using image processing and genetic algorithm”, International Conference on Advances in Computer Engineering and Applications (ICACEA), India, (2015). DOI: https://doi.org/10.1109/ICACEA.2015.7164858
19. [19] Arya, M. S., Anjali, K., Unni, D., “Detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino”, International Conference on Power, Signals, Control and Computation (EPSCICON), India, (2018). DOI: https://doi.org/10.1109/EPSCICON.2018.8379584
20. [20] Li, Y., Wu, S., He, L., Tong, J., Zhao, R., Jia, J., Chen, J., Wu, C., “Development and field evaluation of a robotic harvesting system for plucking high-quality tea”, Computers and Electronics in Agriculture, 206: 107659, (2023). DOI:https://doi.org/10.1016/j.compag.2023.107659
21. [21] Yeshmukhametov, A., Koganezawa, K., Yamamoto, Y., Buribayev, Z., Mukhtar, Z., Amirgaliyev, Y., “Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes”, Applied Sciences, 12(14): 6922, (2022). DOI:https://doi.org/10.3390/app12146922
22. [22] Cap, H. Q., Suwa, K., Fujita, E., Kagiwada, S., Uga, H., Iyatomi, H., “A deep learning approach for on-site plant leaf detection”, IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Malaysia, (2018). DOI: https://doi.org/10.1109/CSPA.2018.8368697
23. [23] Tahir, H. M. H., Khan, S., Tariq, M. O., “Performance Analysis and Comparison of Faster R-CNN, Mask R-CNN and ResNet50 for the Detection and Counting of Vehicles”, International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), India, 587–594, (2021). DOI: https://doi.org/10.1109/ICCCIS51004.2021.9397079
24. [24] Ravichandran, D., Santhanakrishnan, S. A., Sarveshwaran, S., Yogesh, R., “Digital Agriculture: Harnessing IoT and Data Analytics for Smart Farming Solutions”, E3S Web of Conferences, 547: (2024). DOI: https://doi.org/10.1051/e3sconf/202454702003
25. [25] Murugan, M. S., Sungeetha, D., Vijaya, K., Soundari, A. G., Dhanalakshmi, R., Gomathi, S., “Detection and Categorization of Sorghum Crop using MCRNN Architecture”, 4th International Conference on Smart Electronics and Communication (ICOSEC), India, 1505–1509, (2023). DOI: https://doi.org/10.1109/ICOSEC58147.2023.10275812
26. [26] Almeyda, E., Paiva, J., Ipanaqué, W., “Pest Incidence Prediction in Organic Banana Crops with Machine Learning Techniques”, IEEE Engineering International Research Conference (EIRCON), Peru, 1–4, (2020). DOI: https://doi.org/10.1109/EIRCON51178.2020.9254034
27. [27] Kalpana, P., Anandan, R., Hussien, A. G., Migdady, H., Abualigah, L., “Plant disease recognition using residual convolutional enlightened Swin transformer networks”, Scientific Reports, , 14(1): 8660, (2024). DOI: https://doi.org/10.1038/s41598-024-56393-8
28. [28] Demilie, W. B., “Plant disease detection and classification techniques: a comparative study of the performances”, Journal of Big Data, 11: 5, (2024). DOI: https://doi.org/10.1186/s40537-023-00863-9
29. [29] Eunice, J. A., Popescu, D. E., Hemanth, M. K., Chowdary, J. “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications”, Agronomy, 12(10): 2395, (2022). DOI: https://doi.org/10.3390/agronomy12102395
30. [30] Mohanty, S. P., Hughes, D. P., M. Salathé, “Using deep learning for image-based plant disease detection”, Frontiers in Plant Science, 7: 1419, (2016). DOI: https://doi.org/10.3389/fpls.2016.01419
31. [31] Khirade, S. D., Patil, A. B., “Plant Disease Detection Using Image Processing”, International Conference on Computing Communication Control and Automation (ICCUBEA), 768–771, (2015). DOI: https://doi.org/10.1109/ICCUBEA.2015.153
32. [32] Shoaib, M., Shah, V., El-Sappagh, S., Ali, V., Ullah, A., Alenezi, F., Gechev, V., Hussain, T., Ali, F., “An advanced deep learning models-based plant disease detection: A review of recent research”, Frontiers in Plant Science, 14: (2023). DOI: https://doi.org/10.3389/fpls.2023.1158933
33. [33] Kumar, S., Suman, T., Prasad, K. M. V. V., Rao, B. P., Srilekha, A., Naga, J., Krishna, V., “Leaf Disease Detection and Classification based on Machine Learning”, International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 61–365, (2020). DOI: https://doi.org/10.1109/ICSTCEE49637.2020.9277379
34. [34] Kibriya, H., Abdullah, I., Nasrullah, A., “Plant Disease Identification and Classification Using Convolutional Neural Network and SVM”, International Conference on Frontiers of Information Technology (FIT), 264–268, (2021). DOI: https://doi.org/10.1109/FIT53504.2021.00056
35. [35] Loey, M., Elsawy, A., Afify, M., “Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 11(2): 18–41, (2020). DOI: https://doi.org/10.4018/IJSSMET.2020040103
36. [36] Ariwa, R., Markus, C., Teneke, N., Adamu, S., Fumlack, K., “Plant Disease Detection Using Yolo Machine Learning Approach”, British Journal of Computer, Networking and Information Technology, 7: 115–129, (2024). DOI: https://doi.org/10.52589/BJCNIT-EJWGFW6D
37. [37] Tugrul, B., Elhoucine Elfatimi, Eryigit, R., “Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review”, Agriculture, 12(8): 1192, (2022). DOI: https://doi.org/10.3390/agriculture12081192