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

Erhan Kahya; Fatma Özdüven

Makale detayları

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

Yazarlar:

Erhan Kahya | Fatma Özdüven

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Agricultural Engineering (Other) | Precision Agriculture Technologies

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.7 Issue 2, pp.349-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290042

pISSN:

2618-5946

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Erhan Kahya | Fatma Özdüven

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Agricultural Engineering (Other) | Precision Agriculture Technologies

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.7 Issue 2, pp.349-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290042

pISSN:

2618-5946

Deep learning

Lettuce

Robotics

Sayı

Makale detayları

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

Yazarlar: Erhan Kahya | Fatma Özdüven

Yayıncı: Gültekin ÖZDEMİR

Ana Başlıklar: Agricultural Engineering (Other) | Precision Agriculture Technologies

Dergi: International Journal of Agriculture Environment and Food Sciences Vol.7 Issue 2, pp.349-356

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001290042

pISSN: 2618-5946

Deep learning

Lettuce

Robotics

Sayı

Yazarlar:

Erhan Kahya | Fatma Özdüven

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Agricultural Engineering (Other) | Precision Agriculture Technologies

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.7 Issue 2, pp.349-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290042

pISSN:

2618-5946