دانلود مقاله نظارت رشد محصول هوشمند بر حسب سازگاری سیستم و هوش مصنوعی
چکیده
مقدمه
II. مطالب مرتبط
III. سیستم نظارت بر رشد محصول ارائه شده
IV. پیاده سازی و ارزیابی سیستم
نتیجه گیری
منابع
Abstract
I. Introduction
II. Related Work
III. Proposed Crop Growth Monitoring System
IV. System Implementation and Evaluation
V. Conclusion
References
چکیده
این کار یک سیستم نظارت بر رشد محصول هوشمند را پیشنهاد می‌کند که شامل یک موتور رمزنگاری تطبیقی برای اطمینان از امنیت داده‌های حسگر و یک تخمین‌گر مبتنی بر هوش مصنوعی لبه (AI) برای طبقه‌بندی شدت آفات و بیماری (PDS) محصولات هدف است. بر اساس مکانیسم مدیریت سیستم هوشمند، عملکردهای رمزنگاری را می توان با نیازهای مختلف و در زمان واقعی تطبیق داد، در حالی که محرک ها را می توان برای تعامل با دنیای فیزیکی برای اطمینان از رشد سالم محصولات کنترل کرد. آزمایش‌ها نشان می‌دهند زمانی که هر چهار ماژول سخت‌افزار رمزنگاری، از جمله RTEA32، RTEA64، XTEA32 و XTEA64، با استفاده از موتور رمزنگاری تطبیقی پشتیبانی می‌شوند، 72.4 درصد از LUTهای تکه‌ای و 68.4 درصد از رجیسترهای برش بر حسب تراشه Xilinx Zynq20 Zynq-70Z پشتیبانی می‌شوند. نجات یابد. از طریق مکانیزم مدیریت سیستم هوشمند، می توان مصرف برق 0.009 وات را کاهش داد. علاوه بر این، با استفاده از ماژول سخت افزاری شبکه عصبی دوتایی (BNN) برآوردگر PDS، دقت تشخیص محصولات هدف یعنی میوه های اژدها می تواند به 76.57٪ برسد. در مقایسه با طراحی مبتنی بر ریزپردازنده و طراحی شتاب‌دهنده GPU، همان معماری BNN در FPGA می‌تواند فریم‌ها را به ترتیب با ضریب 4919.29 و ضریب 1.08 شتاب دهد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
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.