Özet:

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.

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