Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning

• Published in: IEEE access Vol. 9; pp. 143824 - 143835
• Main Authors: Yu, Helong, Liu, Jiawen, Chen, Chengcheng, Heidari, Ali Asghar, Zhang, Qian, Chen, Huiling, Mafarja, Majdi, Turabieh, Hamza
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cluster analysis; Clustering; Convolution; Convolutional neural networks; Corn; Corn leaf disease diagnosis; Crop diseases; Deep learning; Diagnosis; Diagnostic systems; Diseases; Feature extraction; K-means clustering; Kernel; Machine learning; Medical diagnosis; Plant diseases; transfer Learning; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3120379

Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning model for accurately diagnosing three common diseases of corn leaves: gray spot, leaf spot, and rust. First, to diagnose three diseases, use the K-means algorithm to cluster sample images and then feed them into the improved deep learning model. This paper investigates the impact of various k values (2, 4, 8, 16, 32, and 64) and models (VGG-16, ResNet18, Inception v3, VGG-19, and the improved deep learning model) on corn disease diagnosis. The experiment results indicate that the method has the most significant identification effect on 32-means samples, and the diagnostic recall of leaf spot, rust, and gray spot disease is 89.24 %, 100 %, and 90.95 %, respectively. Similarly, VGG-16 and ResNet18 also achieve the best diagnostic results on 32-means samples, and their average diagnostic accuracy is 84.42% and 83.75%. In addition, Inception v3 (83.05%) and VGG-19 (82.63%) perform best on the 64-means samples. For the three corn diseases, the approach cited in this paper has an average diagnostic accuracy of 93%. It has a more significant diagnostic effect than the other four approaches and can be applied to the agricultural field to protect crops.