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

Erhan Kahya, Fatma Özdüven
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Abstract

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.
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以莴苣(Lactuca Sativa)为例,利用深度学习方法进行机器人育苗系统的育苗选择
莴苣是我国乃至世界上广泛种植和消费的一种蔬菜。苗期是植物生产的开始,是植物最敏感的时期。育苗健康启动生产是保证优质高效生产的重要参数。在本研究中,开发了一个用于苗木生产的机器人系统的自动选苗样本程序。通过开发的程序,目的是在多井盆栽中选择成熟程度相同的幼苗。在本研究中,使用Yolo5n作为训练模型。建立了两种生菜(卷生菜)和红卷生菜叶(lolo-rosso)幼苗的学习系统。经过训练,F1得分为83%;精度为100%;召回率为95%;查全率为86.7%。对于所有给定的图像,学习率为0.0005。这些数据表明,该方法在选苗方面取得了积极的结果。
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审稿时长
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