Comparative Analysis of Machine Learning Techniques Using RGB Imaging for Nitrogen Stress Detection in Maize

Abstract

Proper nitrogen management in crops is crucial to ensure optimal growth and yield maximization. While hyperspectral imagery is often used for nitrogen status estimation in crops, it is not feasible for real-time applications due to the complexity and high cost associated with it. Much of the research utilizing RGB data for detecting nitrogen stress in plants relies on datasets obtained under laboratory settings, which limits its usability in practical applications. This study focuses on identifying nitrogen deficiency in maize crops using RGB imaging data from a publicly available dataset obtained under field conditions. We have proposed a custom-built vision transformer model for the classification of maize into three stress classes. Additionally, we have analyzed the performance of convolutional neural network models, including ResNet50, EfficientNetB0, InceptionV3, and DenseNet121, for nitrogen stress estimation. Our approach involves transfer learning with fine-tuning, adding layers tailored to our specific application. Our detailed analysis shows that while vision transformer models generalize well, they converge prematurely with a higher loss value, indicating the need for further optimization. In contrast, the fine-tuned CNN models classify the crop into stressed, non-stressed, and semi-stressed classes with higher accuracy, achieving a maximum accuracy of 97% with EfficientNetB0 as the base model. This makes our fine-tuned EfficientNetB0 model a suitable candidate for practical applications in nitrogen stress detection.