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 …

Advances in automated and high-throughput imaging technologies have resulted in a
deluge of high-resolution images and sensor data of plants. However, extracting patterns …

Current approaches for accurate identification, classification, and quantification of biotic and
abiotic stresses in crop research and production are predominantly visual and require …

Plant stress phenotyping is essential to select stress-resistant varieties and develop better
stress-management strategies. Standardization of visual assessments and deployment of …

Plant stress is one of major issues that cause significant economic loss for growers. The
labor-intensive conventional methods for identifying the stressed plants constrain their …

During the last decade, there has been rapid adoption of ground and aerial platforms with
multiple sensors for phenotyping various biotic and abiotic stresses throughout the …

Plant phenotyping has been recognized as a bottleneck for improving the efficiency of
breeding programs, understanding plant-environment interactions, and managing …

Background Phenotyping is a critical component of plant research. Accurate and precise trait
collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain …

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 …

In light of growing challenges in agriculture with ever growing food demand across the
world, efficient crop management techniques are necessary to increase crop yield. Precision …