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Plant Leaves

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Cytotoxicity, Antiadipogenic, Low-Density Lipoprotein Oxidation Inhibitory Activities, and Acute Toxicity Study of Hydroethanolic Leaf and Bark Extracts.

TheScientificWorldJournal
Obesity is increasingly taking an important stage as a cause of death worldwide, and interventions with a good cost-effectiveness ratio are needed. is one of these natural products with health benefits. Objective. The present study evaluated the cy...

Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.

PloS one
Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop's growth, re...

A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Journal of hazardous materials
Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study a...

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.

Network (Bristol, England)
Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images ar...

Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.

Scientific reports
This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervas...

Quality prediction of air-cured cigar tobacco leaf using region-based neural networks combined with visible and near-infrared hyperspectral imaging.

Scientific reports
Visible and Near-infrared hyperspectral imaging (VNIR-HSI) combined with machine learning has shown its effectiveness in various detection applications. Specifically, the quality of cigar tobacco leaves undergoes subtle changes due to environmental d...

Tea grading, blending, and matching based on computer vision and deep learning.

Journal of the science of food and agriculture
BACKGROUND: Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This st...

Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning.

Ecotoxicology and environmental safety
Cadmium (Cd) is a heavy metal recognized for its notable biotoxicity. Excessive Cd levels can have detrimental effects on crop growth, development, and yield. Real-time, rapid, and nondestructive monitoring of Cd content in leaves (LCd) is essential ...

Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques.

Sensors (Basel, Switzerland)
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human ...

Increased chloroplast occupancy in bundle sheath cells of rice hap3H mutants revealed by Chloro-Count: a new deep learning-based tool.

The New phytologist
There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing their number and size is a potential route to elevate photosynthetic activity. Notably, bundle shea...