Uzaktan Algılama, Yapay Zeka ve Geleceğin Akıllı Tarım Teknolojisi Trendleri

Öz

Gelecek vadeden bir sektör olarak dijital tarım ve teknolojiler; verimliliği ve üretkenliği iyileştirmeye, biyolojik çeşitliliğin ve toprağın korunmasına, gıda güvenliğinin iyileştirilmesine, sağlık ve beslenmeye, iklim değişliği ile mücadeleye ve kıt kaynaklar üzerindeki baskının azaltılmasına yardımcı olabilir. Akıllı tarımda nesnelerin interneti (IoT), kablosuz sensör ağları (WSN), uzaktan algılama (RS), insansız hava araçları (İHA), büyük veri analitiği, makine öğrenmesi (ML), derin öğrenme (DL) ve yapay zeka (AI) kullanımı, tarım ve endüstrinin uzun ömürlü ve sürdürülebilir olması için kritik öneme sahiptir. AI ve ML tarımda öncelikle verim tahmini, yabancı ot, hastalık, azot ve su stresi tespiti, ürün kalite özelliklerinin tespiti ve sınıflandırılması, bitki türlerinin tanımlanması ve sınıflandırılması gibi bitki yönetimi alanlarında kullanılacağı gibi evapotranspirasyon ve sıcaklık tahmini, toprak kurumasının değerlendirilmesi, toprak sıcaklığı, toprak nemi, sulama zamanı, miktarı ve optimizasyonunun belirlenmesi, toprakta karbon ve azot tahmini gibi toprak ve su yönetiminde öneriler sunabilir. Bu derlemede, tarımı daha verimli hale getirme ve sürdürülebilirlik için WSN, IoT, AI ve ML gibi temel teknolojiler kullanılarak bilginin algılanması, izlenmesi, toplanması, analiz edilmesi ve bilgilerden anlamlı öngörüler çıkarılarak tarımsal faaliyetlerde uygulanabilirliği tartışılmıştır.

Anahtar Kelimeler

• Yapay zeka (AI)
• Makine öğrenmesi (ML)
• Nesnelerin interneti (IoT)
• Tarımsal uzaktan algılama
• Hassas tarım (PA)

Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

Abstract

As a promising sector, digital agriculture and technologies can help improve efficiency, productivity, and food security, protect biodiversity and soil, while also helping to improve food security, nutrition and health, combat climate change and reduce pressure on scarce resources. The use of the internet of things (IoT), wireless sensor networks (WSN), remote sensing (RS), unmanned aerial vehicles (UAVs), big data analytics (BDA), machine learning (ML), deep learning (DL) and artificial intelligence (AI) in smart agriculture is critical for the long-term viability and sustainability of agriculture and industry. In agricultural terms, AI and ML can be used in crop management areas such as yield prediction, weed, disease, nitrogen, and water stress detection, detection and classification of crop quality characteristics, and classification of plant species, as well as suggestions and insights can provided on water management and soil management such as estimation of evapotranspiration and temperature, evaluation of soil drying, estimation of soil temperature and soil moisture, determination of irrigation time, amount and optimization, and prediction of soil carbon and total nitrogen. In this review, its applicability in agricultural activities such as sensing, monitoring, collecting, analysing, and extracting meaningful insights from information by using basic technologies such as WSN, IoT, AI and ML to make agriculture more efficient and sustainable is discussed.

Keywords

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Internet of Things (IoT)
• Agricultural remote sensing
• Precision agriculture (PA)

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