AIMC Topic: Plant Leaves

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Crop leaf disease detection with additive gated convolution and hierarchical attention fusion.

Scientific reports
Crop leaf disease detection plays a crucial role in ensuring healthy crop growth and improving food security. Disease features are often small and have blurry edges, while background interference is strong, making precise detection a significant chal...

Enhancing image based classification for crop disease detection using a multiclass SVM approach with kernel comparison.

Scientific reports
Agricultural production is still quite susceptible to plant diseases, despite the fact that it is essential to both economic growth and food security. Yellow rust can lower wheat yields by 20-30%, red rust by 5-10%, and anthracnose by up to 60% in cr...

Enhanced wheat crop leaf disease classification using multi-level contrast enhancement and modified vision transformers.

Scientific reports
The integration of advanced tools and techniques has significantly boosted agricultural productivity. Wheat crops, which are vital for global food security, are often susceptible to various bacterial and viral diseases, considerably impacting both yi...

An enhanced deep learning-based framework for diagnosing apple leaf diseases.

Scientific reports
Timely and correct identification of diseases in the apple leaf is also important in protecting crop production and sustaining agriculture. This paper introduces E-YOLOv8, a lightweight improved version of YOLOv8, that can be implemented in real-time...

Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.

Scientific reports
Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses an...

An interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning.

Scientific reports
The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. C...

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition.

PloS one
To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases...

A neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves.

Scientific reports
The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention...

Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism.

Scientific reports
The agriculture sector plays a pivotal role in the growth of the global economy, but remains highly susceptible to prediction errors, particularly in disease identification. To address the limitations of existing approaches, this study proposes a dee...

A fusion transfer learning framework for intelligent pest recognition in sustainable agriculture.

Scientific reports
With the fast growth in the population of the world, there is a constantly increasing requirement for sustainable food supplies. Agriculture is the backbone of the global food supply, with vegetables and fruits being essential for a balanced intake. ...