Image-Based Plant Seedling Classification Using Ensemble Learning

Agriculture is crucial for human survival and is a major economic engine across the world, particularly in emerging countries. Plant seed classification is a multi-class dataset with 5,539 pictures divided into 12 classes. We investigate various learning classifiers for the image-based multi-class problem in this study. We will start with a simple convolutional neural network (CNN) classifier model and work our way up to more complex options like support vector machines, and K-nearest neighbors. We will create an ensemble of classifiers to increase the current state-of-the-art accuracy. We will also investigate data preprocessing techniques like segmentation, masking, and feature engineering for an improvement in the overall precision. We will compare the performance as well as their impact on the final ensemble. To overcome this challenge, traditional techniques use complex convolution layer-based neural network architectures like Resnet and VGG-19. Though these techniques are effective, there is still scope for increasing accuracy. In this study, we propose a boosting ensemble-based strategy that employs a multilayer CNN model with a deep convolution layer that is boosted using the K-nearest neighbors lazily supervised learning technique. Although the fact that this combination is less complex than previous ways, it has obtained a higher accuracy of 99.90%.