As advancements in agricultural technology unfold, machine learning and deep learning
approaches are gaining interest in robust plant disease identification. Early disease …

This chapter highlights the importance of automated plant disease analysis for agricultural
enhancement. It addresses two critical challenges: disease identification and severity …

This research aims to introduce a groundbreaking framework for the automated detection of
plant diseases leveraging the power and potential of Convolutional Neural Networks …

In recent years, the global population has shown substantial growth, leading to an increase
in its food security needs. In response, greenhouse cultivation has emerged as a strategy to …

Plant diseases have a significant impact on agricultural productivity and global food security.
Accurate and timely identification of plant diseases is crucial for effective disease …

The advancement of deep learning techniques has revolutionized the field of computer
vision and enabled the development of sophisticated systems for plant disease detection …

Plant disease detection has played a significant role in combating plant diseases that pose a
threat to global agriculture and food security. Detecting these diseases early can help …

Plant diseases pose a significant threat to global food production and are a major challenge
for crop management. Deep learning techniques have demonstrated remarkable success in …

Plants can be affected by various diseases. As such, the early detection of crop diseases
plays an essential role in the farming industry. However, such detection requires extensive …

Detecting plant diseases is crucial as they can significantly impact plant growth. At the same
time, several machine-learning methodologies have been employed to distinguish and …