Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

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 …

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

Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Identifying spatial distribution of crop planting in large-scale is one of the most significant
applications of remote sensing imagery. As an active remote sensing system, synthetic …

Reliable estimation of region-wide rice yield is vital for food security and agricultural
management. Field-scale models have increased our understanding of rice yield and its …

In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …

Paddy rice is a staple food of three billion people in the world. Timely and accurate
estimation of the paddy rice planting area and paddy rice yield can provide valuable …