论文标题
食品食谱建议基于成分检测，使用深度学习
Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning
论文作者
论文摘要
食物对于人类生存至关重要，人们总是试图品尝不同类型的美味食谱。通常，人们甚至在不知道自己的名字或拿起一些食物成分的情况下选择食品成分，而这些食物对他们来说并不明显。了解哪些成分可以混合以制造美味的食物食谱。对于初学者厨师来说，选择合适的食谱非常困难。但是，即使对于专家来说，这也可能是一个问题。这样的示例是通过图像处理识别对象。尽管由于食品成分的不同，该过程很复杂，但传统方法将导致不准确率。这些问题可以通过机器学习和深度学习方法来解决。在本文中，我们实施了食品成分识别模型，并设计了一种算法，用于推荐基于公认成分的食谱。我们制作了一个自定义数据集，该数据集由9856张图像组成，属于32种不同的食品成分类。卷积神经网络（CNN）模型用于识别食物成分，并且为了提出食谱建议，我们使用了机器学习。我们的准确度为94％，这是令人印象深刻的。
Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that are not obvious to them from a grocery store. Knowing which ingredients can be mixed to make a delicious food recipe is essential. Selecting the right recipe by choosing a list of ingredients is very difficult for a beginner cook. However, it can be a problem even for experts. One such example is recognising objects through image processing. Although this process is complex due to different food ingredients, traditional approaches will lead to an inaccuracy rate. These problems can be solved by machine learning and deep learning approaches. In this paper, we implemented a model for food ingredients recognition and designed an algorithm for recommending recipes based on recognised ingredients. We made a custom dataset consisting of 9856 images belonging to 32 different food ingredients classes. Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations, we have used machine learning. We achieved an accuracy of 94 percent, which is quite impressive.
