The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …

The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

Agriculture plays a vital role in the economic growth of any country. With the increase of
population, frequent changes in climatic conditions and limited resources, it becomes a …

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used
to improve agriculture productivity while reducing drudgery, inspection time, and crop …

Abstract Machine learning is an important decision support tool for crop yield prediction,
including supporting decisions on what crops to grow and what to do during the growing …

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

Modern agriculture and food production systems are facing increasing pressures from
climate change, land and water availability, and, more recently, a pandemic. These factors …

Automatic detection and analysis of rice crop diseases is widely required in the farming
industry, which can be utilized to avoid squandering financial and other resources, reduce …

Early prediction of grain yield helps scientists to make better breeding decisions for wheat.
Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based …