AIMC Topic: Plant Leaves

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Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling.

Sensors (Basel, Switzerland)
Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine lear...

Prediction of barberry witches' broom rust disease using artificial intelligence models: a case study in South Khorasan, Iran.

Scientific reports
The South Khorasan Province in Iran is the main producer of seedless barberry, accounting for 98% of the country's production. This has led to significant economic growth in the region. However, the cultivation of barberry is threatened by the rust f...

Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases.

Scientific reports
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images di...

A deep learning-based approach for the detection of cucumber diseases.

PloS one
Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditi...

A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture.

Scientific reports
Plant pathogens and pests hinder general plant health, resulting in poor agricultural yields and production. These threaten global food security and cause environmental and economic shortages. Amidst the available existing heavy deep learning (DL) mo...

Safety assessment of the ethanolic extract of Siparuna guianensis: Cell viability, molecular risk predictions and toxicity risk for acute and sub-chronic oral ingestion.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: The species Siparuna guianensis Aublet (family Siparunaceae) is traditionally used by indigenous peoples and riverine communities in Central and South America to treat migraines, flu, respiratory diseases, fever, pain,...

Advancing plant leaf disease detection integrating machine learning and deep learning.

Scientific reports
Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the diffic...

Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Scientific reports
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...

Detection of kidney bean leaf spot disease based on a hybrid deep learning model.

Scientific reports
Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages,...

Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection.

Scientific reports
Rice is susceptible to various diseases, including brown spot, hispa, leaf smut, bacterial leaf blight, and leaf blast, all of which can negatively impact crop yields. Current disease detection methods encounter several challenges, such as reliance o...