RTAL: An edge computing method for real-time rice lodging assessment

Rice is a globally important crop that plays an important role in feeding more and more of the world's population as we cope with climate change and population growth. Rice lodging is one of the main yield-reducing factors in rice production, which also has a direct impact on rice quality and leads to harvest difficulties, so it is very important to obtain lodging data in time. Lodging assessment is a tedious task that generally requires a lot of time and labor due to the large area of land involved. This study proposes an edge computing method for rice lodging areas suitable for unmanned aerial vehicles (UAVs) without post-processing, called Real Time Rice Lodging Area Calculation Method (RTAL). The RTAL method is based on deep learning and photogrammetry, and can calculate large-scale farmland lodging areas in real time using only edge computing devices. Tested on the edge computing device Nvidia Jetson Xavier NX, when the ground resolution is 0.1 m, the fastest prediction speed can reach 14417.9 m2/s. When the operation time of a single sortie is 80 min, the predicted area can reach 10 km2. This method greatly improves the efficiency of prediction, and can provide real-time help in rice yield measurement or disaster damage assessment.

Automated summary

This study proposes an edge computing method based on deep learning and photogrammetry for real-time calculation of rice lodging areas using unmanned aerial vehicles (UAVs). The method shows high efficiency and real-time capabilities, providing assistance in rice yield measurement and disaster damage assessment.