Smartphone-based detection of leaf color levels in rice plants

Abstract

Leaf color is correlated with nitrogen content, and detection of nitrogen content in rice leaves is important for guiding farmers in applying fertilizer. However, the performance of existing detection methods highly depends on the field environmental condition. Also, these methods require special imaging and computing equipment. To fill these gaps, a smartphone app was developed based on a standard leaf color chart (LCC) to detect color levels of rice leaves. Using the app developed, regions of rice leaf and LCC in an image were successfully identified by the color threshold segmentation. The color features of each region were effectively extracted using the CIELAB histograms. The color difference values between leaf and LCC that were calculated by the CIEDE2000 formula could be used to differentiate the color levels of rice leaves. The app was tested in field conditions. The results were accurate 96% of the times. Compared with manual inspections, the accuracies of the smartphone app in determining the color levels of rice leaves were higher than 92%. The average runtime for processing a leaf image in a field condition was 248 ms when using a Xiaomi Mi5 smartphone. The app also worked well after being implemented in other smartphones. The smartphone app allowed for an accurate, time-efficient, and low-cost detection of rice leaf color levels, which will help farmers in making decisions related to nitrogen fertilizer management for rice production.