AIMC Topic: Plant Leaves

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Towards practical AI for agriculture: A self-supervised attention framework for Spinach leaf disease detection.

PloS one
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learni...

Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling.

Scientific reports
Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to ...

Optimised MobileNet for very lightweight and accurate plant leaf disease detection.

Scientific reports
The development of accurate and efficient plant disease classification systems is vital for addressing the challenges of climate change and the growing global demand for food. This study presents [Formula: see text]PlantNet, a novel lightweight multi...

Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice.

Planta
This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding. Metabolites serve as dyn...

Early diagnosis of transient ischemic attack facilitated by SERS-based artificial intelligence sensors.

Mikrochimica acta
Transient ischemic attack (TIA) serves as a critical early warning sign for ischemic stroke. Its timely identification holds significant clinical value in reducing recurrence risk and improving patient prognosis. However, existing detection methods e...

High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.

Scientific reports
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniqu...

Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features.

Scientific reports
The prevalence of diseases in wheat crops poses a significant threat to global food security, as it reduces yield and quality. Addressing these challenges is critical for sustainable agriculture. This study proposes and evaluates a hybrid deep learni...

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

Scientific reports
Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened ...

Ensemble-based feature fusion for accurate plant disease classification using pre-trained models.

Scientific reports
Agricultural productivity remains seriously threatened by the attacks of plant diseases, even though it is the bedrock of global food security. These diseases, if ignored, can lead to massive crop losses and economic setbacks. Therefore, the developm...

OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.

Scientific reports
Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of f...