Crop disease detection is important due to its significant impact on agricultural productivity
and global food security. Traditional disease detection methods often rely on labour …

Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to
prevent the diseases from spreading, minimize the need for chemical pesticides and …

Strawberries are susceptible to various diseases during their growth, and leaves may show
signs of diseases as a response. Given that these diseases generate yield loss and …

Plant leaf diseases severely impair crop quality and productivity. Accurate segmentation of
diseases facilitates the understanding of disease distribution and is a critical step in …

Apples are susceptible to postharvest bruises, leading to a shortened shelf life and
significant waste. Therefore, accurate detection of apple bruises is crucial to mitigate food …

Deep learning approaches have been widely applied for agricultural disease detection.
However, considerable challenges still exist, such as low recognition accuracy in complex …

Maize diseases caused by fungal pathogens are the primary factor resulting in reduced
maize yield. However, in practical complex background scenarios, diseases caused by …

Introduction Precise semantic segmentation of microbial alterations is paramount for their
evaluation and treatment. This study focuses on harnessing the SegFormer segmentation …

This study proposes a deep learning-based method for monitoring the growth of Botrytis
cinerea and evaluating the effectiveness of control measures. It aims to address the …

Maize (Zea mays) is a critical crop for global food security and economic stability. However,
it is highly vulnerable to various diseases such as northern leaf blight, common rust, and …