Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant
stress symptoms, yet data analysis remains a key challenge. Data challenges include …

The agricultural sector faces increasing challenges in ensuring food security and optimizing
crop yield, necessitating innovative solutions for early detection and mitigation of plant …

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the
visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) …

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 …

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 …

Plants undergo stress whenever they are subjected to adverse conditions or an element that
inhibits metabolism and growth (Lichtenthaler, 1996). Plants incur irreversible harm, even …

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

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

Current methods of in-house plant phenotyping are providing a powerful new tool for plant
biology studies. The self-constructed and commercial platforms established in the last few …

Agriculture has undergone a remarkable transformation, transitioning from traditional
methods that were used for centuries to technology-driven practices. The advent of image …