Deep learning assisted real-time nitrogen stress detection for variable rate fertilizer applicator in wheat crop

Abstract

An early and rapid detection of nitrogen (N) stress in field crops is crucial to mitigating nutrient deficiency and achieving sustainable crop yield. Although numerous methods and equipment have been developed to monitor crop N stress and fertilizer application thereof, many of these technologies face significant limitations in terms of costs, accuracy, integration, etc. This study reports the development of a Variable Rate fertilizer Application (VRA) system assisted by Deep Learning (DL) model deployed embedded system to enable rapid RGB image-based detection of nitrogen stress in wheat crop and subsequent application of N fertilizer. AlexNet DL model resulted in precision, recall, and F1-score as 0.977, 0.973, and 0.973, respectively; for classifying N stress into three classes. The developed VRA could operate in sync with embedded system at an operational speed of 0.4 m/s with a field capacity of 0.32 ha/h in a 26 DAS wheat crop. The effectivity of the VRA was evaluated by vegetation indices (ExG, RGRI, VARI and NGRDI) with drone assisted RGB images before and after VRA operation; there was a consistent difference in before and after average index values for ExG (0.2046 and 0.2917) and VARI (0.1478 and 0.2454). These results are indicative of the uniformity of operation by VRA throughout the field. The average percentage N fertilizer saving under VRA as compared to traditional technique was 37.53 % with an insignificant (p < 0.05) difference in yield. This study delivers a real-time effective technique for precise classification of N stress and its real-time mechanized management in wheat crop.