Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography

Fei, Shuaipeng; Xiao, Shunfu; Li, Qing; Shu, Meiyan; Zhai, Weiguang; Xiao, Yonggui; Chen, Zhen; Yu, Helong; Ma, Yuntao

doi:10.1016/j.compag.2023.108462

Cited by 5 publications

(1 citation statement)

References 52 publications

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“…Impollonia et al [128] also estimated the leaf area index and chlorophyll content using an RF and gaussian process regression, respectively. RGB, multispectral, and thermal images can also be merged for feature extraction of canopy structures and vegetation indices calculation [129]. The Deep Neural Network (DNN), a more robust and sophisticated model, was used to analyse RGB, multispectral, and thermal images to predict soybean yield [126].…”

Section: Crop Managementmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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Section: Crop Managementmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

View full text Add to dashboard Cite

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Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Zhu,

Zhang,

Yang

et al. 2024

Precision Agric

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Use of Unmanned Aerial Vehicles for Monitoring Pastures and Forages in Agricultural Sciences: A Systematic Review

Santos,

Martins,

Bezerra

et al. 2024

Drones

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With the growing demand for efficient solutions to face the challenges posed by population growth and climate change, the use of unmanned aerial vehicles (UAVs) emerges as a promising solution for monitoring biophysical and physiological parameters in forage crops due to their ability to collect high-frequency and high-resolution data. This review addresses the main applications of UAVs in monitoring forage crop characteristics, in addition to evaluating advanced data processing techniques, including machine learning, to optimize the efficiency and sustainability of agricultural production systems. In this paper, the Scopus and Web of Science databases were used to identify the applications of UAVs in forage assessment. Based on inclusion and exclusion criteria, the search resulted in 590 articles, of which 463 were filtered for duplicates and 238 were selected after screening. An analysis of the data revealed an annual growth rate of 35.50% in the production of articles, evidencing the growing interest in the theme. In addition to 1086 authors, 93 journals and 4740 citations were reviewed. Finally, our results contribute to the scientific community by consolidating information on the use of UAVs in precision farming, offering a solid basis for future research and practical applications.

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Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography

Cited by 5 publications

References 52 publications

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Use of Unmanned Aerial Vehicles for Monitoring Pastures and Forages in Agricultural Sciences: A Systematic Review

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