Starches are important energy sources found in plants with many uses in the pharmaceutical industry such as binders, disintegrants, bulking agents in drugs and thus require very careful physicochemical analysis for proper identification and verification which includes microscopy. In this work, we applied artificial intelligence techniques (using transfer learning and deep convolution neural network CNNs to microscopical images obtained from 9 starch samples of different botanical sources. Our approach obtained an accuracy of 61% when the machine learning model was pretrained on microscopic images from MicroNet dataset. However the accuracy jumped to 81% for model pretrained on random day to day images obtained from Imagenet dataset. The model pretrained on the imagenet dataset also showed a better precision, recall and f1 score than that pretrained on the imagenet dataset.

翻译：淀粉是植物中重要的能量来源，在制药工业中具有多种用途，如用作药物中的粘合剂、崩解剂和填充剂，因此需要通过包括显微镜检查在内的严格理化分析进行准确鉴定与验证。本研究应用人工智能技术（利用迁移学习和深度卷积神经网络CNN）对来自9种不同植物来源的淀粉样本的显微图像进行分析。当机器学习模型使用MicroNet数据集的显微图像进行预训练时，其准确率达到61%。然而，当模型使用ImageNet数据集的日常随机图像进行预训练时，准确率跃升至81%。基于ImageNet数据集预训练的模型在精确率、召回率和F1分数方面也优于基于MicroNet数据集预训练的模型。