Pratondo Agus, Bramantoro Arif
School of Applied Sciences, Telkom University, Bandung, West Java, Indonesia.
School of Computing and Informatics, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam.
PeerJ Comput Sci. 2022 Apr 7;8:e884. doi: 10.7717/peerj-cs.884. eCollection 2022.
and are popular larvae as feed ingredients that are widely used by animal lovers to feed reptiles, songbirds, and other poultry. These two larvae share a similar appearance, however; the nutritional ingredients are significantly different. is more nutritious and has a higher economic value compared to . Due to limited knowledge, many animal lovers find it difficult to distinguish between the two. This study aims to build a machine learning model that is able to distinguish between the two. The model is trained using images that are taken from a standard camera on a mobile phone. The training is carried on using a deep learning algorithm, by adopting an architecture through transfer learning, namely VGG-19 and Inception v3. The experimental results on the datasets show that the accuracy rates of the model are 94.219% and 96.875%, respectively. The results are quite promising for practical use and can be improved for future works.
[此处原文中两种幼虫名称缺失，无法准确翻译]和[此处原文中两种幼虫名称缺失，无法准确翻译]是受欢迎的幼虫饲料成分，被动物爱好者广泛用于喂养爬行动物、鸣禽和其他家禽。然而，这两种幼虫外观相似；营养成分却有显著差异。与[此处原文中两种幼虫名称缺失，无法准确翻译]相比，[此处原文中两种幼虫名称缺失，无法准确翻译]营养更丰富，经济价值更高。由于知识有限，许多动物爱好者难以区分这两者。本研究旨在构建一个能够区分两者的机器学习模型。该模型使用手机上标准相机拍摄的图像进行训练。通过采用迁移学习架构，即VGG - 19和Inception v3，使用深度学习算法进行训练。数据集上的实验结果表明，该模型的准确率分别为94.219%和96.875%。结果对于实际应用很有前景，并且可以在未来的工作中进一步改进。
