Recent applications of machine learning, remote sensing, and iot
  approaches in yield prediction: a critical review
Fatima Zahra Bassine Terence Epule Epule Ayoub Kechchour Abdelghani Chehbouni
The integration of remote sensing and machine learning in agriculture is
transforming the industry by providing insights and predictions through data
analysis. This combination leads to improved yield prediction and water
management, resulting in increased efficiency, better yields, and more
sustainable agricultural practices. Achieving the United Nations' Sustainable
Development Goals, especially "zero hunger," requires the investigation of crop
yield and precipitation gaps, which can be accomplished through, the usage of
artificial intelligence (AI), machine learning (ML), remote sensing (RS), and
the internet of things (IoT). By integrating these technologies, a robust
agricultural mobile or web application can be developed, providing farmers and
decision-makers with valuable information and tools for improving crop
management and increasing efficiency. Several studies have investigated these
new technologies and their potential for diverse tasks such as crop monitoring,
yield prediction, irrigation management, etc. Through a critical review, this
paper reviews relevant articles that have used RS, ML, cloud computing, and IoT
in crop yield prediction. It reviews the current state-of-the-art in this field
by critically evaluating different machine-learning approaches proposed in the
literature for crop yield prediction and water management. It provides insights
into how these methods can improve decision-making in agricultural production
systems. This work will serve as a compendium for those interested in yield
prediction in terms of primary literature but, most importantly, what
approaches can be used for real-time and robust prediction.