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پیشبینی رطوبت خاک گلخانه با استفاده از شبکه عصبی مصنوعی و حسگرهای شبکه بیسیم | ||
| مهندسی بیوسیستم ایران | ||
| دوره 53، شماره 4، بهمن 1401، صفحه 341-356 اصل مقاله (1.95 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2023.341992.665482 | ||
| نویسندگان | ||
| فائزه بهزادی پور1؛ محمود قاسمی نژاد رائینی* 2؛ سامان آبدانان مهدی زاده2؛ مرتضی تاکی2؛ بیژن خلیلی مقدم3؛ محمدرضا زارع بوانی4 | ||
| 1دانشجوی دکتری، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران | ||
| 2دانشیار دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران | ||
| 3دانشیار دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران | ||
| 4استادیار دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران | ||
| چکیده | ||
| رطوبت خاک یکی از اصلیترین عوامل تعیین کننده برای رشد بهتر گیاهان به ویژه در گلخانهها که امروزه بصورت گستردهای مورد استقبال قرار گرفتهاند، میباشد. اندازهگیری رطوبت خاک و عوامل محیطی بصورت پیوسته و سالانه، علاوه بر وقتگیر بودن، هزینههای زیادی را در پی دارد. از اینرو یکی از ابزارهای پیشبینی کننده هوشمند که امروزه با کمترین میزان خطا جایگاه وسیعی در علم کشاورزی به خود اختصاص داده، ابزار شبکه عصبی میباشد. در این پژوهش به منظور کنترل رطوبت خاک توسط نقشه پیشبینی رطوبت مبتنی بر شبکه عصبی مصنوعی، درصد رطوبت و دمای خاک، میزان نور، دما و رطوبت محیط در گلخانهای واقع در شمال شرقی خوزستان، طی دو فصل زمستان و بهار اندازهگیری و ثبت گردید. نتایج نشان از پیشبینی دقیق نقشه رطوبتی خاک در فصل زمستان و بهار به ترتیب با کمترین میزان خطای استاندارد (12/1 و 71/1) و بالاترین ضریب تعیین (R2) به ترتیب 94/0 و 78/0 بین مقادیر اندازهگیری شده واقعی و مقادیر پیشبینی شده در مرحله آموزش و بالاترین ضریب تعیین در مرحله آزمایش برای فصل زمستان و بهار به ترتیب 87/0 و 93/0 توسط شبکه عصبی مصنوعی داشتند. بنابراین دقت قابل توجه در پیشبینی رطوبت خاک توسط این نرمافزار نشان از اهمیت و قابلیت اطمینان بالای آن در امور کشاورزی و گلخانهای دارد که به موجب آن، کنترل رطوبت خاک آسانتر گردیده و تنشهای رطوبتی کمتری برای خاک و به تبع آن برای گیاه رخ میدهد. | ||
| کلیدواژه‌ها | ||
| پیش &rlm؛ بینی؛ شبکه عصبی مصنوعی؛ گلخانه؛ نقشه رطوبت خاک؛ هوشمند | ||
| عنوان مقاله [English] | ||
| Predictions of greenhouse soil moisture using artificial neural network and wireless network sensing | ||
| نویسندگان [English] | ||
| Faeze Behzadipour1؛ Mahmoud Ghaseminezhad2؛ Saman Abdanan Mehdizadeh2؛ Morteza Taki2؛ Bijan Khalili Moghadam3؛ Mohammad reza Zare Bavani4 | ||
| 1PhD student, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran | ||
| 2Associate Professor, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran | ||
| 3Associate Professor, Faculty of Agriculture,, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran | ||
| 4Assistance Professor, Faculty of Agriculture, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran | ||
| چکیده [English] | ||
| Soil moisture is one of the main factors determining the better growth of plants which are widely well-received today, especially in greenhouses. Measuring the soil moisture and the environmental factors has high costs continuously and annually, in addition to being time-consuming. Therefore, one of the intelligent predictive tools that have a vast position in agricultural science is the neural network tool with the least amount of error. In this study, soil moisture and temperature percentage, light, ambient temperature, and humidity in a greenhouse located in northeastern Khuzestan were Measured and recorded during two seasons of winter and spring to control soil moisture by a moisture prediction map based on an artificial neural network. The results show an accurate forecast of soil moisture map in winter and spring between actual values that were measured and values that were predicted with the lowest standard error (1.12 and 1.71) and the highest coefficient of determination (R2) of 0.94 and 0.78, respectively, and the highest coefficient of determination were 0.87 and 0.93, respectively, by the artificial neural network in the experimental stage for winter and spring. Therefore, the remarkable accuracy in the prediction of soil moisture by this software shows its importance and high reliability in agriculture and greenhouses which makes it easier to control soil moisture and less moisture stress occurs for soil and the plant consequently. | ||
| کلیدواژه‌ها [English] | ||
| Forecast, Artificial Neural Network, Greenhouse, Soil Moisture Map, Smart | ||
| مراجع | ||
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