Artificial Intelligence-Based Fertilizer and Crop Forecasting in Smart Agriculture with the Help of the Internet of Things

Zülküf Güman; Faruk Baturalp Gunay

Makale detayları

Artificial Intelligence-Based Fertilizer and Crop Forecasting in Smart Agriculture with the Help of the Internet of Things

Yazarlar:

Zülküf Güman | Faruk Baturalp Gunay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.958-973

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001325071

pISSN:

2536-4618

Anahtar kelimeler:

Özet:

 In agricultural activities, it is very important to get high-yield harvests at low costs. To get a high yield from the harvest, it is necessary to choose products and fertilizers suitable for the agricultural land. Getting high yields with low costs in agriculture is also possible with smart agriculture. With smart agriculture, agricultural activity stages can be controlled; Precautions can also be taken against negativities that may arise from external factors. To control agricultural lands remotely; Internet of Things IoT based sensors require hardware systems to receive data from these sensors and send them to the server. The data sent to the server is evaluated with artificial intelligence algorithms and the need for the land is determined according to the result and the need for fertilizer, irrigation need, etc. suitable for the agricultural stage is determined. Processing is carried out according to needs. For this purpose, in this study, hardware products, including field and server modules, were developed to capture sensor data from agricultural land with IoT and send it to the server. Data sets taken from open-access websites were used to train models with machine learning methods, one of the fields of artificial intelligence. The data taken from the field is evaluated with the created machine learning models, allowing the selection of products and fertilizers suitable for the land.

 In agricultural activities, it is very important to get high-yield harvests at low costs. To get a high yield from the harvest, it is necessary to choose products and fertilizers suitable for the agricultural land. Getting high yields with low costs in agriculture is also possible with smart agriculture. With smart agriculture, agricultural activity stages can be controlled; Precautions can also be taken against negativities that may arise from external factors. To control agricultural lands remotely; Internet of Things IoT based sensors require hardware systems to receive data from these sensors and send them to the server. The data sent to the server is evaluated with artificial intelligence algorithms and the need for the land is determined according to the result and the need for fertilizer, irrigation need, etc. suitable for the agricultural stage is determined. Processing is carried out according to needs. For this purpose, in this study, hardware products, including field and server modules, were developed to capture sensor data from agricultural land with IoT and send it to the server. Data sets taken from open-access websites were used to train models with machine learning methods, one of the fields of artificial intelligence. The data taken from the field is evaluated with the created machine learning models, allowing the selection of products and fertilizers suitable for the land.

 In agricultural activities, it is very important to get high-yield harvests at low costs. To get a high yield from the harvest, it is necessary to choose products and fertilizers suitable for the agricultural land. Getting high yields with low costs in agriculture is also possible with smart agriculture. With smart agriculture, agricultural activity stages can be controlled; Precautions can also be taken against negativities that may arise from external factors. To control agricultural lands remotely; Internet of Things IoT based sensors require hardware systems to receive data from these sensors and send them to the server. The data sent to the server is evaluated with artificial intelligence algorithms and the need for the land is determined according to the result and the need for fertilizer, irrigation need, etc. suitable for the agricultural stage is determined. Processing is carried out according to needs. For this purpose, in this study, hardware products, including field and server modules, were developed to capture sensor data from agricultural land with IoT and send it to the server. Data sets taken from open-access websites were used to train models with machine learning methods, one of the fields of artificial intelligence. The data taken from the field is evaluated with the created machine learning models, allowing the selection of products and fertilizers suitable for the land.

 Tarımsal faaliyetlerde düşük maliyetler yüksek verimli hasat almak oldukça önemlidir. Hasattan yüksek verim almak için de tarım arazisine uygun ürün ve gübre seçimi yapmak gerekmektedir. Tarımda düşük maliyetler ile yüksek verim alma da akıllı tarım ile mümkün olabilmektedir. Akıllı tarım ile tarımsal faaliyet aşamaları kontrol edilebildiği gibi; dış etkenlerden gelebilecek olumsuzluklara karşı da önlem alınabilmektedir. Tarım arazilerini uzaktan kontrol edebilmek için; nesnelerin interneti IoT tabanlı sensörler, bu sensörlerden veri alıp sunucuya göndermek için donanımsal sistemlere ihtiyaç vardır. Sunucuya gönderilen verilerde yapay zekâ algoritmaları ile değerlendirilip sonuca göre arazinin ihtiyacı belirlenir ve tarım aşamasına uygun gübre ihtiyacı, sulama ihtiyacı vb. ihtiyaçlara göre işlem yapılır. Bu amaçla bu çalışmada tarım arazisinden IoT ile sensör verilerinin alınıp sunucuya göndermek amaçlı arazi ve sunucu modülü olmak üzere donanımsal ürünler yapılmıştır. Yapay zekâ alanlarından biri olan makine öğrenmesi yöntemleri ile modelleri eğitmek için açık erişimli internet sitelerinden alınan veri setleri kullanılmıştır. Araziden alınan veriler oluşturulan makine öğrenmesi modelleri ile değerlendirilip araziye uygun ürün ve gübre seçimi yapılmasına olanak sağlanmaktadır.

Makale detayları

Artificial Intelligence-Based Fertilizer and Crop Forecasting in Smart Agriculture with the Help of the Internet of Things

Yazarlar:

Zülküf Güman | Faruk Baturalp Gunay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.958-973

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001325071

pISSN:

2536-4618

Smart farming

Artificial intelligence

Machine learning

İnternet of things

Crop Selection

Fertilizer SelectionAkıllı tarım

Yapay Zekâ

Makine öğrenmesi

Nesnelerin interneti

Mahsül seçimi

Gübre Seçimi

Sayı

Makale detayları

Yazarlar: Zülküf Güman | Faruk Baturalp Gunay

Yayıncı: Igdir University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.958-973

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001325071

pISSN: 2536-4618

Sayı

Yazarlar:

Zülküf Güman | Faruk Baturalp Gunay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.958-973

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001325071

pISSN:

2536-4618