پیش‏‌بینی رطوبت خاک گلخانه با استفاده از شبکه عصبی مصنوعی و حسگرهای شبکه بی‏سیم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران

2 دانشیار دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران

3 دانشیار دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران

4 استادیار 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

موضوعات


عنوان مقاله [English]

Predictions of greenhouse soil moisture using artificial neural network and wireless network sensing

نویسندگان [English]

  • Faeze Behzadipour 1
  • Mahmoud Ghaseminezhad 2
  • Saman Abdanan Mehdizadeh 2
  • Morteza Taki 2
  • Bijan Khalili Moghadam 3
  • Mohammad reza Zare Bavani 4
1 PhD student, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran
2 Associate Professor, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran
3 Associate Professor, Faculty of Agriculture,, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
4 Assistance 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
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