Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

T Wen, JH Li, Q Wang, YY Gao, GF Hao… - Science of The Total …, 2023 - Elsevier
Plant phenotyping is important for plants to cope with environmental changes and ensure
plant health. Imaging techniques are perceived as the most critical and reliable tools for …

Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass (Lolium perenne L.)—A review

C Jayasinghe, P Badenhorst, J Jacobs… - Grass and Forage …, 2021 - Wiley Online Library
Perennial ryegrass (Lolium perenne L.) is considered the most important pasture species in
temperate agriculture, with over six million hectares of sown area in Australia alone …

[PDF][PDF] Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

SC Lauguico, RIIS Concepcion… - International Journal of …, 2020 - academia.edu
Smart aquaponics management is significant in providing efficient resource consumption.
For this to realize, cultivars' evaluation is a necessary step to identify the system's …

Estimation of nitrogen content in winter wheat based on multi-source data fusion and machine learning

F Ding, C Li, W Zhai, S Fei, Q Cheng, Z Chen - Agriculture, 2022 - mdpi.com
Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N
content in winter wheat can effectively monitor the crop growth status. The objective of this …

New approach for predicting nitrogen and pigments in maize from hyperspectral data and machine learning models

BC da Silva, R de Mello Prado, FHR Baio… - Remote Sensing …, 2024 - Elsevier
Fast diagnostics from hyperspectral data and machine learning (ML) models to predict
nitrogen (N) and pigment content in maize crops is challenging to optimize nitrogen …

Application of meta-heuristic feature selection method in low-cost portable device for watermelon classification using signal processing techniques

A Alipasandi, A Mahmoudi, B Sturm, H Behfar… - … and Electronics in …, 2023 - Elsevier
Recognizing the stage of fruit maturity and thus determining the optimum harvesting time is
critical since the competitive market requires high-quality products at a competitive price …

The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen

WR Mancin, LET Pereira, RSB Carvalho… - Revista Ciência …, 2021 - SciELO Brasil
This study is based on the principle that vegetation indexes (VIs), derived from the RGB
color model obtained from digital images, can be used to characterize spectral signatures …

Deep learning-based model for classification of bean nitrogen status using digital canopy imaging

MM Baesso, L Leveghin, EJS Sardinha… - Engenharia …, 2023 - SciELO Brasil
Laboratory chemical analysis of leaf samples can be costly and time-consuming, making it
impractical for assessing crop variability. To address this challenge, researchers have …

Deep Learning-Enabled Mobile Application for On-Site Nitrogen Prediction in Strawberry Cultivation

N Singh, V Mahore, S Kaur, K Ajaykumar… - Journal of Biosystems …, 2024 - Springer
Purpose Precisely applying nitrogen to plants is important for their optimal growth,
preventing overuse that may cause water pollution, soil degradation, and financial losses …

Study of the chemical composition of Urochloa brizantha using the SPAD index, neural networks, multiple linear models, principal components and cluster analysis

FF Simili, KRS Barbosa, JG Augusto… - Animal Feed Science …, 2019 - Elsevier
The objectives of this study were to explore the relationship between plant variables using
correlation and principal component analysis; to explore the chemical composition patterns …