Image Classification of Wheat Rust Based on Ensemble Learning

Abstract

Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.

Investigation on the use of ensemble learning and big data in crop identification

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Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds

“…Using this method on the test dataset, Inception-v3 achieved the highest recognition accuracy of 92.5%. Pan [16] proposed a method for identifying wheat rust based on ensemble learning (WR-EL) and enhanced the stochastic gradient descent with the warm restarts algorithm (SGDR-S). The recognition accuracy of WR-EL increased by 32%, 19%, 15%, 11%, and 8%, respectively, when compared with five CNN models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201.…”

A Method for Sugarcane Disease Identification Based on Improved ShuffleNetV2 Model

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