Applying Multiple Deep Models to Predict Plant Pests in Advanced Agriculture

Abstract

Nowadays, advanced sciences and technologies have been wide applied to smart agriculture fields. There are many challenges to agricultural companies, and scientists. So that it is important task to investigate a solution to detect early of plant pests and diseases for appropriately treating to product green agriculture products with least environmental impacts. This paper presents a proposed approach for applying artificial intelligence, deep learning specifically, to classify some plant pests and diseases. We have investigated a number of deep architectures of machine learning for effective solution for pests prediction through agriculture images. Some deep neural models are studied to apply for feature extraction task. Particularly, we surveyed and experimented based on some well-known architectures such as ResNet, EfficientNet, MobileNet, NASNet. In the classified part, we proposed the use of fully connected neural network. To evaluation and analyze the performance effectiveness of the proposed approach, we collected plant image pests and diseases in agriculture circumstance. Dataset consists of 3,391 samples within 6 categories of plant pests and diseases. Generally, there is also imbalance problem of the plant pest samples in some categories. Therefore, we also applied data augmentation solutions to improve the accuracy of the prediction system. Experimental results show that the pest prediction approach based on deep learning techniques reaches high accuracy. Among of them the feature extraction backbone based on ResNet101 conducts the highest results with the ratios of accuracy, precision, recall, specificity and F1 are 99,25%, 97,84%, 97,83%, 99,53% and 97,82%, respectively.