Addressing the challenges of high model complexity, low generalization capability, and
suboptimal detection performance in most algorithms for crop leaf disease detection, the …

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs)
have enabled researchers to significantly improve the accuracy and speed of object …

Background: Plant diseases affect agricultural output, quality and profitability, making them
serious obstacles for agriculture. It is essential to detect diseases early in order to reduce …

Apple is a significant economic crop in China, and leaf diseases represent a major
challenge to its growth and yield. To enhance the efficiency of disease detection, this paper …

The automated collection of plant phenotypic information has become a trend in breeding
and smart agriculture. Four YOLOv8-based models were used to segment mature soybean …

In a country like India, where most of the income depends on agriculture, it is challenging to
rely only on soil-based agriculture in the future. These days, soil-based agriculture faces …

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea
diseases in low-light environments and the difficulty in detecting small lesions, a novel …

This study introduces an innovative approach to enhance greenhouse management through
the integration of YOLOv8, for the detection of pests and categorization of diseases in tomato …

For small target objects on fast-moving conveyor belts, traditional vision detection algorithms
equipped with conventional robotic arms struggle to capture the long and short-range pixel …

Urban air mobility (UAM) represents a burgeoning mode of air transportation, presently in its
nascent operational phase with manned aircraft that necessitate pilot assistance. The …