An example of lettuce (Lactuca Sativa) seedling selection using deep learning method for robotic seedling selection system

Yıl 2023, Cilt: 7 Sayı: 2, 349 - 356, 29.06.2023

Erhan Kahya , Fatma Özdüven

https://doi.org/10.31015/jaefs.2023.2.13

Cited By: 1

Öz

Lettuce is a type of vegetable that is widely cultivated and consumed in our country and in the world. The seedling period, which is the beginning of production, is the most sensitive time for the plant. Starting production with healthy seedlings is an important parameter for quality and efficient production. In this study, a sample program for automatic seedling selection was developed for a robotic system to be used in seedling production. With the developed program, it was aimed to select seedlings with the same degree of maturity in multi-well pots. In this study, Yolo5n was used for the training model. A learning system was established on two types of lettuce (curly salad), and red curly lettuce leaf (lolo-rosso) seedlings. As a result of the training, F1 score was found as 83%; Precision was 100%; Recall was 95%; Precision Recall was 86.7%. The learning rate was 0.0005 for all given images. In view of these data, positive results were obtained for the mentioned method in seedling selection.

Anahtar Kelimeler

Deep learning, Lettuce, Robotics

Kaynakça

• Ahsan, M., Eshkabilov, S., Cemek, B., Küçüktopcu, E., Lee, C. W., & Simsek, H. (2022). Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.). Sustainability, 14(1). https://doi.org/10.3390/su14010416
• Akdemir, S. (2018). Marul (Lactuca sativa L.) fide kalitesi ve bitki gelişimi üzerine paclobutrazol ve prohexadione-calcium uygulamalarının etkileri. Yüksek Lisans Tezi, Kırşehir Ahi Evran Üniversitesi. Fen Bilimleri Enstitüsü, Kırşehir. (in Turkish)
• Alon, A. S. (2020). Machine Vision Recognition System for Iceberg Lettuce Health Condition on Raspberry Pi 4b: A Mobile Net SSD v2 Inference Approach. International Journal of Emerging Trends in Engineering Research, 8(4), 1073–1078. https://doi.org/10.30534/ijeter/2020/20842020
• Anonymous (2023a). https://tr.wikipedia.org/wiki/Marul (Access Time:20.01.2023)
• Anonymous (2023b). https://www.researchgate.net/publication/359094711_Turkiye’de_Marul _Yetistiriciligi_ Sorunlari_ve_Cozum_Onerileri (Access Time:20.01.2023)
• Balkaya, A., Özgen, R. (2019). Marul Tarımı. Tarım Gündem Dergisi, s. 9-11. (in Turkish)
• Doğru Ş. M., Çilingir A. (2019). Marul Tarımı. Tarım Gündem Dergisi, s. 26-29. (in Turkish)
• Du, J., Lu, X., Fan, J., Qin, Y., Yang, X., & Guo, X. (2020).” Image-Based High-Throughput Detection and Phenotype Evaluation Method for Multiple Lettuce Varieties.”, Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.563386
• Hassim, S. A., & Chuah, J. H. (2020). Lettuce classification using convolutional neural network. Food Research, 4, 118–123. https://doi.org/10.26656/fr.2017.4(S6).029
• Lu, J. Y., Chang, C. L., & Kuo, Y. F. (2019). Monitoring growth rate of lettuce using deep convolutional neural networks. 2019 ASABE Annual International Meeting. https://doi.org/10.13031/aim.201900341
• Rizkiana, A., Nugroho, A. P., Salma, N. M., Afif, S., Masithoh, R. E., Sutiarso, L., & Okayasu, T. (2021). Plant growth prediction model for lettuce (Lactuca sativa.) in plant factories using artificial neural network. IOP Conference Series: Earth and Environmental Science, 733(1). https://doi.org/10.1088/1755-1315/733/1/012027
• Sevgican, A. (2002). Örtüaltı Sebzeciliği. Cilt I, E.Ü. Zir. Fak. Yay., No:528, 476 s. (in Turkish
• Vural, H., Eşiyok D., & Duman İ. (2000). Kültür Sebzeleri; Sebze Yetiştirme. Ege Üniversitesi Ziraat Fakültesi, Bahçe Bitkileri Bölümü, Bornova-İzmir, 440 s. (in Turkish
• Yelboğa, K., 2014. Tarımın büyüyen gücü: Fide sektörü. Bahçe Haber Dergisi, 3(2):13-16. (in Turkish
• Yıldırım, M., Bahar, E., & Demireli, K. (2015). The effects of different irrigation levels on the yield and physical properties of lettuce cultivars (Lactuca sativa var.campania). COMU Journal of Agriculture Faculty, 3(1), 29-34.
• Yudha Pratama, I., Wahab, A., & Alaydrus, M. (2020, November 3). Deep learning for assessing unhealthy lettuce hydroponic using convolutional neural network based on faster R-CNN with Inception V2. 2020 5th International Conference on Informatics and Computing, ICIC 2020. https://doi.org/10.1109/ICIC50835.2020.9288554
• Zhang, L., Xu, Z., Xu, D., Ma, J., Chen, Y., & Fu, Z. (2020). Growth monitoring of greenhouse lettuce based on a convolutional neural network. Horticulture Research, 7(1). https://doi.org/10.1038/s41438-020-00345-6