AI Medical Compendium Journal:
Frontiers in plant science

Showing 1 to 10 of 63 articles

Integrating AI detection and language models for real-time pest management in Tomato cultivation.

Frontiers in plant science
Tomato ( L.) cultivation is crucial globally due to its nutritional and economic value. However, the crop faces significant threats from various pests, including , , and , among others. These pests not only reduce yield but also increase production c...

Rapid and accurate classification of mung bean seeds based on HPMobileNet.

Frontiers in plant science
Mung bean seeds are very important in agricultural production and food processing, but due to their variety and similar appearance, traditional classification methods are challenging, to address this problem this study proposes a deep learning-based ...

Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images.

Frontiers in plant science
Accurate counting of crop plants is essential for agricultural science, particularly for yield forecasting, field management, and experimental studies. Traditional methods are labor-intensive and prone to errors. Unmanned Aerial Vehicle (UAV) technol...

Unveiling genetic basis of seedling emergence from deep soil depth under dry direct- seeded conditions in rice ( L.).

Frontiers in plant science
Water scarcity and labor shortage pose significant challenges in rice farming. Direct-seeded rice (DSR) is an efficient method that conserves water, reduces labor costs, and allows for full mechanization of cultivation. However, variable planting dep...

Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms.

Frontiers in plant science
The leaf area index (LAI) is a critical parameter for characterizing plant foliage abundance, canopy structure changes, and vegetation productivity in ecosystems. Traditional phenological measurements are often destructive, time-consuming, and labor-...

Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat ( L.).

Frontiers in plant science
Phytochromes are essential photoreceptors in plants that sense red and far-red light, playing a vital role in regulating plant growth and development through light signal transduction. Despite extensive research on phytochromes in model plants like ...

An image dataset for analyzing tea picking behavior in tea plantations.

Frontiers in plant science
Tea is an important economic product in China, and tea picking is a key agricultural activity. As the practice of tea picking in China gradually shifts towards intelligent and mechanized methods, artificial intelligence recognition technology has bec...

Estimation of leaf area index by combining multi-source remote sensing data and machine learning optimization model.

Frontiers in plant science
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a pr...

Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning.

Frontiers in plant science
Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter whea...

Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation.

Frontiers in plant science
Spectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has...