Abstract

     This work proposes a smart crop growth monitoring system that contains an adaptive cryptography engine to ensure the security of sensor data and an edge artificial intelligence (AI) based estimator to classify the pest and disease severity (PDS) of target crops. Based on the smart system management mechanism, cryptographic functions can be adapted to varying and real-time requirements, while the actuators can be controlled to interact with the physical world to ensure the healthy growth of crops. Experiments show when all the four cryptographic hardware modules, including RTEA32, RTEA64, XTEA32 and XTEA64, are supported, using the adaptive cryptography engine, 72.4% of slice LUTs and 68.4% of slice registers in terms of the Xilinx Zynq-7000 XC7Z020 chip can be saved. Through the smart system management mechanism, a power consumption of 0.009 watts can be reduced. Furthermore, using the binarized neural network (BNN) hardware module of the PDS estimator, the recognition accuracy of target crops i.e. dragon fruits can achieve 76.57%. Compared to the microprocessor-based design and the GPU accelerated one, the same BNN architecture on the FPGA can accelerate the frames per second by a factor of 4,919.29 and a factor of 1.08, respectively.

Introduction

     Recently, the abnormal climate leads to the extreme weather, while the occurrence of natural disasters such as typhoon, rainstorm and severe drought gradually increases. This causes great casualties and serious damages to our properties and environment. For agriculture, the extreme weather also makes the growth of crops unstable, and the problem of food shortage thus becomes more and more serious. For all countries in the world, the food crisis has also become a very important issue.

     Until now, most crops are still planted in the outdoor. This means the growth of crops will be affected by the weather easily. This also makes the yield and quality of farm crops unstable. Compared to the opening planting environments, recently, the greenhouse becomes a new alternative due to its controllable advantage. With the incoming of agriculture 4.0, new techniques such as cyber physical systems (CPS) [1] and Internet-of-Things (IoT) [2] further enhance the efficiency of the agricultural management. Furthermore, with the popularity of big data analytics [3], the trend of crop growth can be predicted and analyzed. For example, by applying sensors to the planting environment of crops, the collected data can be further analyzed to improve the productivity and quality of crops. Furthermore, the corresponding actuators such as sprinklers can be also controlled to interact with the physical world to ensure the healthy growth of crops.

Conclusion

     To monitor the crop growth efficiently, this work proposes a crop growth monitoring system based on system adaptivity and edge AI. The presented adaptive cryptography engine can not only support varying requirements of cryptographic functions but also provide real-time decryption processing of sensor data. Furthermore, the layered and virtualizable design makes the crop growth monitoring system scalable. The edge AI based PDS estimator provides real-time detection of the target crops, while the image fusion method can assist in classifying the level of PDS. Through the smart system management mechanism along with the adaptive cryptography engine and the PDS estimator, the actuators can be controlled to interact with the physical world to ensure the healthy growth of crops. Our experiments also demonstrated the practicability and applicability of the proposed design.