Abstract
The escalating demands of human reproduction and consumption have placed immense pressure on agricultural companies to enhance production speed and size. However, rapid growth in agricultural operations has led to an increase in human errors, affecting the quality and safety of agricultural products. To mitigate these repercussions, agro-businesses are exploring the potential of artificial intelligence (AI) technologies, specifically computer vision, to detect anomalies and assess the health of agricultural produce. This research paper focuses on utilizing YOLO-based object detection to evaluate the health of apples by detecting common diseases and disorders. The proposed methodology involves training three versions of YOLO models (YOLOv5s, YOLOv5m, and YOLOv7) using different optimization algorithms. The performance results show that YOLOv5m achieved the highest mean average precision (mAP) score of 89% using SGD with Nesterov momentum.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lasri, I., Riadsolh, A., Elbelkacemi, M.: Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learning. Educ. Inform. Technol. 28(4), 4069–4092 (2023). https://doi.org/10.1007/s10639-022-11370-4
Lasri, I., Riadsolh, A., El Belkacemi, M.: Toward an effective analysis of COVID-19 Moroccan business survey data using machine learning techniques. In: Proceedings of the 13th International Conference on Machine Learning and Computing, pp. 50–58 (2021). https://doi.org/10.1145/3457682.3457690
Seema, K.A., Gill, G.S.: Automatic fruit grading and classification system using computer vision: a review. In: Proceedings of the 2015 Second International Conference on Advances in Computing and Communication Engineering, pp. 598–603 (2015). https://doi.org/10.1109/ICACCE.2015.15
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7790–788. Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.91
Tan, M., Pang, R., Le, Q.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781–10790 (2020)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT), pp. 177–186 (2010)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980v9
Nesterov, Y.: A method of solving a convex programming problem with convergence rate o(1/k2). Soviet Math. Doklady 27(2), 372–376 (1983)
Kaggle. https://www.kaggle.com/. Accessed 24 March 2023
Apple Disease Detection Image Dataset. https://universe.roboflow.com/pfe-ns5wl/apple-disease-detection-jspkx/dataset/1. Accessed 24 March 2023
Roboflow. https://roboflow.com/. Accessed 26 March 2023
Thakkar, F., Saha, G., Shahnaz, C., Hu, Y.-C.: In: Proceedings of the International E-Conference on Intelligent Systems and Signal Processing, Advances in Intelligent Systems and Computing, Springer, Singapore, pp. 511–522 (2022)
Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., Marinello, F.: Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms. Agronomy 12(2), 319 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lasri, I., Douiri, S.M., El-Marzouki, N., Riadsolh, A., Elbelkacemi, M. (2024). YOLO-Based Approach for Intelligent Apple Crop Health Assessment. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-031-48573-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-48573-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48572-5
Online ISBN: 978-3-031-48573-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)