Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds

Abstract

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy-eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one-dimensional convolutional neural network (1D-CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two-dimensional convolutional neural network (2D-CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high-throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.

High-Oleic Sunflower Oil as a Potential Substitute for Palm Oil in Sugar Coatings—A Comparative Quality Determination Using Multispectral Imaging and an Electronic Nose

“…In previous studies, both devices were used for food- and feed-quality control. Multispectral imaging was used for seed-quality classification [ 16 , 17 , 18 ], observing the fermentation status of water kefir [ 19 ], performing quality assessment in butter cookies [ 11 , 20 ] or identifying adulteration in coffee [ 21 ] or meat [ 22 ]. Nondestructive measurements were also performed on lean pork slices to successfully identify bone fragments [ 14 ].…”

Multi-classification recognition of blood cell images based on transfer learning

“…In contrast to machine learning, deep learning is end-to-end in nature, requiring little to no feature selection and image pre-processing, and the network can input images directly and complete feature extraction and classification in one step. This feature has helped deep learning algorithms develop quickly, and convolutional neural networks for medical image analysis have become a significant area of research [3] . In this paper, transfer learning is introduced to implement an end-to-end classification algorithm using convolutional neural networks for feature extraction as well as image classification of blood cell images.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…The ML methods are applied in two main categories: (1) supervised method by predicting some output variable associated with each input sample and (2) unsupervised method that does not need any sample data and provides a prediction by considering input feature dataset. The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45]. Although the ML methods have provided promising results in many abovementioned applications, they suffer from lower coverage and generalization [1].…”