Application of Convolutional Neural Networks for Watermelon Detection in UAV Aerial Images: A Case Study

Abstract

Although human dependence on agriculture decreases with developing technology, it continues. As many resources are increasingly restricted due to various climatic reasons, the importance of studies in this field increases. Applications using deep learning models are frequently encountered in the agricultural field. In particular, there are applications where deep learning models are used as a tool for optimum planting, land use, yield improvement, production/disease/pest control, and other activities.In this study, watermelons in an aerial view of a watermelon field were detected by utilizing the Alexnet deep learning architecture. To obtain yield, watermelons in watermelon fields should be specified and then counted. Aerial images are used for this application. The field image was divided into 50% overlapping sub-images, and each was classified as watermelon, leaf, and soil. Consequently, watermelon regions on the field image were specified. After training the Alexnet and Vgg19 network structure with the dataset, watermelons were to be identified by segmenting the images. It was observed that the Vgg19 network achieved 97.78% accuracy. The results of the experimental applications show that the Vgg19 can be applied for watermelon fruit and yield detection applications.

Keywords

• Watermelon
• Alexnet
• Vgg19
• Classification
• Agriculture

References

1. Ağin, O., Malasli, M. Z., & Tarihi, G. (2016). Görüntü İşleme Tekniklerinin Sürdürülebilir Tarımdaki Yeri ve Önemi: Literatür Çalışması. Journal of Agricultural Machinery Science, 12(3), 199–206.
2. Aydın, V. A. (2024). Comparison of CNN-based methods for yoga pose classification. Turkish Journal of Engineering, 8(1), 65–75. https://doi.org/10.31127/TUJE.1275826
3. Pajaziti, A., Basholli, F., & Zhaveli, Y. (2023). Identification and classification of fruits through robotic systems by using artificial intelligence. Engineering Applications, 2(2), 154–163.
4. Hyder, U., & Talpur, M. R. H. (2024). Detection of cotton leaf disease with machine learning model. Turkish Journal of Engineering, 8(2), 380–393.
5. Koç, D. G., & Vatandaş, M. (2021). Classification of Some Fruits using Image Processing and Machine Learning. Turkish Journal of Agriculture - Food Science and Technology, 9(12), 2189–2196. https://doi.org/10.24925/TURJAF.V9I12.2189-2196.4445
6. Wan Nurazwin Syazwani, R., Muhammad Asraf, H., Megat Syahirul Amin, M. A., & Nur Dalila, K. A. (2022). Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning. Alexandria Engineering Journal, 61(2), 1265–1276. https://doi.org/10.1016/J.AEJ.2021.06.053
7. Akar, Ö, Saralioğlu, E., Güngör, O.,& Bayata, H. F.(2024). Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences, 9(1), 12-24 https://doi.org/10.26833/ijeg.1252298
8. Gholami, A. (2024). Exploring drone classifications and applications: a review. International Journal of Engineering and Geosciences, 9 (3), 418-442 https://doi.org/10.26833/ijeg.1428724

9. Villi, O., & Yakar, M. (2024). Sensor technologies in unmanned aerial vehicles: types and applications.Advanced UAV, 4(1), 1-18
10. Mukasa, P., et al. (2022). Nondestructive discrimination of seedless from seeded watermelon seeds by using multivariate and deep learning image analysis. Computers and Electronics in Agriculture, 194, 106799. https://doi.org/10.1016/J.COMPAG.2022.106799
11. Zheng, Y. Y., Kong, J. L., Jin, X. B., Wang, X. Y., Su, T. L., & Zuo, M. (2019). CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture. Sensors, 19(5), 1058. https://doi.org/10.3390/S19051058
12. Jayakumar, D., Elakkiya, A., Rajmohan, R., & Ramkumar, M. O. (2020). Automatic Prediction and Classification of Diseases in Melons using Stacked RNN based Deep Learning Model. 2020 International Conference on System, Computation, Automation and Networking, ICSCAN 2020. https://doi.org/10.1109/ICSCAN49426.2020.9262414
13. Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. https://doi.org/10.1016/J.COMPAG.2020.105393
14. Abu-Nasser, S. S. A. N., & Bassem, S. (2018). Rule-Based System for Watermelon Diseases and Treatment. International Journal of Academic Information Systems Research (IJAISR), 2(7), 1-7. https://www.researchgate.net/publication/326927417_Rule-Based_System_for_Watermelon_Diseases_and_Treatment
15. Nazulan, W. N. S. W., Asnawi, A. L., Ramli, H. A. M., Jusoh, A. Z., Ibrahim, S. N., & Azmin, N. F. M. (2020). Detection of Sweetness Level for Fruits (Watermelon) with Machine Learning. 2020 IEEE Conference on Big Data and Analytics, ICBDA 2020, 79–83. https://doi.org/10.1109/ICBDA50157.2020.9289712
16. Koc, A. B. (2007). Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biology and Technology, 45(3), 366–371. https://doi.org/10.1016/J.POSTHARVBIO.2007.03.010
17. Jiang, G., Wang, Z., & Liu, H. (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 42(5), 2429–2441. https://doi.org/10.1016/J.ESWA.2014.10.033
18. Maharlooei, M., Sivarajan, S., Bajwa, S. G., Harmon, J. P., & Nowatzki, J. (2017). Detection of soybean aphids in a greenhouse using an image processing technique. Computers and Electronics in Agriculture, 132, 63–70. https://doi.org/10.1016/J.COMPAG.2016.11.019
19. Fen, İ. Ü., Enst, B., & Der, K. (2022). Kadir SABANCI ve ark. Retrieved June 30, 2022, from http://www.tuik.gov.tr/bitkiselapp/
20. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16(8), 1222. https://doi.org/10.3390/S16081222
21. Ekiz, A., Arica, S., & Bozdogan, A. M. (2019). Classification and Segmentation of Watermelon in Images Obtained by Unmanned Aerial Vehicle. ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering, 619–622. https://doi.org/10.23919/ELECO47770.2019.8990605
22. Capizzi, G., Lo Sciuto, G., Napoli, C., Tramontana, E., & Wozniak, M. (2015). Automatic classification of fruit defects based on co-occurrence matrix and neural networks. undefined, 861–867. https://doi.org/10.15439/2015F258
23. Makalesi, A., & Aslan, M. (2021). Derin Öğrenme ile Şeftali Hastalıklarının Tespiti. European Journal of Science and Technology, 23, 540–546. https://doi.org/10.31590/ejosat.883787
24. Doğan, İ. T. F. (2018). The Comparison Of Leaf Classification Performance Of Deep Learning Algorithms. SAKARYA UNIVERSITY JOURNAL OF COMPUTER AND INFORMATION SCIENCES, 1.
25. Salamon, J., & Bello, J. P. (2017). Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, 24(3), 279–283. https://doi.org/10.1109/LSP.2017.2657381
26. Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020). A Review of Convolutional Neural Network Applied to Fruit Image Processing. Applied Sciences, 10(10), 3443. https://doi.org/10.3390/APP10103443
27. Aydın, V. A.(2024). Comparison of CNN-based methods for yoga pose classification. Turkish Journal of Engineering, 8 (1), 65-75.
28. İnik, Ö., et al. (2022). GAZİOSMANPAŞA BİLİMSEL ARAŞTIRMA DERGİSİ (GBAD). Gaziosmanpasa Journal of Scientific Research Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Retrieved July 5, 2022, from http://dergipark.gov.tr/gbad Yakar, M., & Dogan, Y. (2019). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 73-75). Springer International Publishing. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. Retrieved April 2017, from https://doi.org/10.48550/arxiv.1704.06857
29. Hemmer, M., Van Khang, H., Robbersmyr, K. G., Waag, T. I., & Meyer, T. J. J. (2018). Fault classification of axial and radial roller bearings using transfer learning through a pretrained convolutional neural network. Designs, 2(4), 1–17. https://doi.org/10.3390/DESIGNS2040056
30. Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A., & Catheline, G. (2018). 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. Retrieved September 15, 2022, from http://arxiv.org/abs/1801.05968 Kanun, E., Alptekin, A., Karataş, L., & Yakar, M. (2022). The use of UAV photogrammetry in modeling ancient structures: A case study of “Kanytellis”. Advanced UAV, 2(2), 41-50. Alvarez-Canchila, O. I., Arroyo-Pérez, D. E., Patiño-Saucedo, A., Rostro González, H., & Patĩo-Vanegas, A. (2020). Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning. Journal of Physics: Conference Series, 1547(1), 012020. https://doi.org/10.1088/1742-6596/1547/1/012020
31. Yeh, J. F., Lin, K. M., Lin, C. Y., & Kang, J. C. (2021). Intelligent Mango Fruit Glade Classification Using AlexNet with GrabCut Algorithm. 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. https://doi.org/10.1109/ICCE-TW52618.2021.9603074
32. Han, X., Zhong, Y., Cao, L., & Zhang, L. (2017). Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sensing, 9(8), 848. https://doi.org/10.3390/RS9080848
33. Patino-Saucedo, A., Rostro-Gonzalez, H., & Conradt, J. (2018). Tropical fruits classification using an alexnet-type convolutional neural network and image augmentation. Lecture Notes in Computer Science, 11304, 371–379. https://doi.org/10.1007/978-3-030-04212-7_32/COVER
34. Mascarenhas, S., & Agarwal, M. (2021). A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. https://doi.org/10.1109/CENTCON52345.2021.9687944
35. Bansal, M., Kumar, M., Sachdeva, M., & Mittal, A. (2023). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing, 14(4). https://doi.org/10.1007/s12652-023-08495-5
36. Doğuş Gülgün, O., & Erol, H. (2020). Classification performance comparisons of deep learning models in pneumonia diagnosis using chest X-ray images. Turkish Journal of Engineering (TUJE), 4, 129–141. https://doi.org/10.31127/TUJE.652358
37. Şahin, N., Alpaslan, N., İlçin, M., & Hanbay, D. (2023). Evrişimsel sinir ağı mimarileri ve öğrenim aktarma ile bitki zararlısı çekirge türlerinin sınıflandırması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(1), 321–331. https://doi.org/10.35234/FUMBD.1228883
38. Alibabaei, K., et al. (2022). A review of the challenges of using deep learning algorithms to support decision-making in agricultural activities. Remote Sensing, 14(3). https://doi.org/10.3390/RS14030638
39. Iqbal, A., Usman, M., & Ahmed, Z. (2022). An efficient deep learning-based framework for tuberculosis detection using chest X-ray images. Tuberculosis, 136, 102234. https://doi.org/10.1016/J.TUBE.2022.102234
40. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics, p. 738). Springer. Available online: https://www.springer.com/gp/book/9780387310732
41. Gulzar, Y. (2023). Fruit image classification model based on MobileNetV2 with deep transfer learning technique. Sustainability (Switzerland), 15(3), 1906. https://doi.org/10.3390/SU15031906
42. Asriny, D. M., & Jayadi, R. (2023). Transfer learning VGG16 for classification of orange fruit images. Journal of System and Management Sciences, 13(1). https://doi.org/10.33168/JSMS.2023.0112
43. Chen, H. C., et al. (2022). AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electronics, 11(6), 951. https://doi.org/10.3390/ELECTRONICS11060951
44. Nguyen, T. H., Nguyen, T. N., & Ngo, B. V. (2022). A VGG-19 model with transfer learning and image segmentation for classification of tomato leaf disease. AgriEngineering, 4(4). https://doi.org/10.3390/agriengineering4040056
45. Zhu, H., Yang, L., Fei, J., Zhao, L., & Han, Z. (2021). Recognition of carrot appearance quality based on deep feature and support vector machine. Computers and Electronics in Agriculture, 186, 106185. https://doi.org/10.1016/J.COMPAG.2021.106185