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

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 …

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 …

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 …

Drought stress detection involves multi-modal image analysis with high spatio-temporal
resolution. Identification of digital traits that characterizes drought stress response (DSR) is …

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

Deep learning techniques have been successfully deployed for automating plant stress
identification and quantification. In recent years, there is a growing push towards training …

Data augmentation is a powerful tool for improving deep learning‐based image classifiers
for plant stress identification and classification. However, selecting an effective set of …

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 …

The agriculture sector is still working to mitigate the detrimental effects of plant diseases on
crop production and food security. Accurate and prompt disease diagnosis is essential to …