Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile
tool to assimilate large amounts of heterogeneous data and provide reliable predictions of …

With the rapid development of genetic analysis techniques and crop population size,
phenotyping has become the bottleneck restricting crop breeding. Breaking through this …

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for
strawberries, which would allow them to allocate optimal labor and equipment, as well as …

With climate change and ever-increasing population growth, the pace of varietal
development needs to be accelerated in order to feed a population of 10 billion by 2050 …

Phenotyping plants is an essential component of any effort to develop new crop varieties. As
plant breeders seek to increase crop productivity and produce more food for the future, the …

The identification of water stress is a major challenge for timely and effective irrigation to
ensure global food security and sustainable agriculture. Several direct and indirect methods …

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that
leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and …

Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.
The high-throughput field phenotyping systems and in particular robotic systems play an …

Advanced farming techniques help to know the appropriate environmental conditions, soil
quality, water and fertilizer needs, and crop monitoring during each plant's growth phase …