Agriculture is one of the most crucial sectors, meeting the fundamental food needs of
humanity. Plant diseases increase food economic and food security concerns for countries …

This review explores the applications of Convolutional Neural Networks (CNNs) in smart
agriculture, highlighting recent advancements across various applications including weed …

Tomato is one of the most popular and most important food crops consumed globally. The
quality and quantity of yield by tomato plants are affected by the impact made by various …

Abstract Machine learning has revolutionized the field of agricultural science, particularly in
the early detection and management of plant diseases, which are crucial for maintaining …

In contemporary agriculture, farmers confront substantial challenges in maintaining crop
yields and mitigating agricultural losses attributable to diseases. The existing methods for …

Tomato blight significantly threatened tomato yield and quality, making precise disease
detection essential for modern agricultural practices. Traditional segmentation models often …

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis
and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery …

Diagnosing plant diseases is a difficult task, but it could be made easier with the use of
advanced instrumentation and the latest machine learning techniques. This paper is a …

Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges
from various sources, including environmental (abiotic stress) and biological (biotic stress or …

This study presents an improved DeepLabV3+ model named AS-DeepLabV3+, specifically
designed for segmenting disease spots on apple leaves. AS-DeepLabV3+ addresses critical …