The Common Rust Detection in Corn by Utilization of Hybrid Deep Learning Algorithm VGG-16

The Common Rust (CR) is a disorder in corn plantations which is caused due to the fungus Puccinia sorghi, common rust in corn is characterized by the small, reddish-brown pustules that develop on the leaves, and often start forming in the rows. These pustules developed on the leaves, with time turn black, with the yellowing surrounding tissue, potentially reducing the crop yield. Common rust in the corn plantation reduces photosynthesis, leading to the stunted growth and lower yields. Severe infections can cause the premature leaf death, the weakened plants, and increased vulnerability to other diseases and environmental stressors. Common rust in corn thrives in the cool and moist conditions, leading to the increased fungicide use, soil contamination, and potential water pollution. The reduced yields may also drive land expansion, contributing to the deforestation and habitat loss. This work introduces a deep-learning classifier designed to differentiate between two main types of image data, the corn leaves affected by common rust disease and healthy corn leaves. The images undergo de-noising to improve quality. The cleaned data is then used to train VGG-16 algorithm based model, and it achieves high accuracy. In summary, this research aims to develop a highly accurate image classifier for detecting common rust disease in corn, and it achieves an accuracy rate of 99.47%.