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 …

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 research on the application of remote sensing and deep learning‐based analysis in
precision agriculture demonstrated a potential for improved crop management and reduced …

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 …

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

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 …

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

Agriculture, an essential bedrock of human survival, continually grapples with the menace of
plant diseases, culminating in substantial yield reductions. While conventional detection …

Several factors associated with disease diagnosis in plants using deep learning techniques
must be considered to develop a robust system for accurate disease management. A …

As compared with the traditional visual discrimination methods, deep learning and image
processing methods have the ability to detect plants efficiently and non-invasively. This is of …