Efficient crop management and treatment rely on early detection of plant stress. Imaging
sensors provide a non-destructive and commonly used method for detecting stress in large …

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 …

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

For farmers, the variety of crop diseases and symptoms causes great difficulties in the
diagnosis of plant diseases. To address this problem, researchers have recently begun …

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

Stress due to nutrients deficiency in plants can reduce the agricultural yield significantly.
Nitrogen, an essential nutrient, is a crucial growth-limiting factor and is the prime component …

Recent years have seen a significant development in plant stress phenotyping and
classification systems based on deep learning, multimodal analysis, and computer vision …

For farmers, the variety of crop diseases and symptoms causes great difficulties in the
diagnosis of plant diseases. To address this problem, researchers have recently begun …

The use of deep networks in agriculture has increased enormously in the last decade
including their use to classify different plant leaf stresses. More recently, a large number of …

Background Abiotic stressors impair crop yields and growth potential. Despite recent
developments, no comprehensive literature review on crop abiotic stress assessment …