Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

Astor, Thomas; Dayananda, Supriya; Nautiyal, Sunil; Wachendorf, M.

doi:10.3390/agronomy10101600

Cited by 26 publications

(8 citation statements)

References 60 publications

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“…In [7], hyperspectral images of broccoli provided quantitative parameters for detecting glucosinolates levels. In [45], two cameras were used for estimating vegetable crop biomass. RGB three-dimensional (3D) was attached to UAV for acquiring height information and terrestrial hyperspectral imagery was used for obtaining reflectance information on cabbage, tomato, and eggplant.…”

Section: Hyperspectral Cameramentioning

confidence: 99%

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Ali¹,

Deng²,

Shaikh³

et al. 2024

CSSE

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Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AIbased UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detection methods are discussed. Investigations show the significance of using UAV swarms for growth monitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits. KEYWORDSArtificial intelligence (AI); Brassica; disease identification; image analysis; image processing; machine learning (ML); monitoring; swarm; remote sensors; unmanned aerial vehicles (UAVs) UAV Swarm Brassica Plants ML Approaches Research FocusFigure 1: Review framework with the research focusWe searched articles in different databases, such as Science Direct, Google Scholar, SpringerLink, SAGE Journals, IEEE Xplore, and Wiley Online Library Journal. In the advanced search section of these database repositories, the period from 2018 to 2023 was selected. While plotting the framework we ensured to include all the important studies. After selecting the first batch of research papers, their related articles were also searched. To extract relevant articles, we read the abstract of all the retrieved articles. The screening criteria include deleting non-English and duplicate papers. We included the papers which were focused on optical sensors and machine learning approaches. We excluded the papers which were focused on other remote sensing tools and sensors. This review is proposed for farmers and researchers who are inclined to study Brassica plants and UAV swarms. The major contributions of the paper include:

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Section: Hyperspectral Cameramentioning

confidence: 99%

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Ali¹,

Deng²,

Shaikh³

et al. 2024

CSSE

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“…It can also be combined with field data to more efficiently assess spatial and temporal distributions of crop parameters (Aneece et al, 2017;Afrasiabian et al, 2021). Over the past three decades, remote sensing techniques have been used as very useful tools to precisely monitor crops throughout their growing season to support decisions for good agricultural practices (Astor et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A critical review on applications of hyperspectral remote sensing in crop monitoring

Kong

Hou

et al. 2022

Ex. Agric.

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Summary Numerous technologies have contributed to the recent development of agriculture, especially the advancement in hyperspectral remote sensing (HRS) constituted a revolution in crop monitoring. The widespread use of HRS to obtain crop parameters suggests the need for a review of research advances in this area. HRS offers new theories and methods for studying crop parameters, but much work needs to be done both experimentally and theoretically before we can truly understand the physical and chemical processes that predict these crop parameters. The study focuses on the following elements: 1) The article provides a relatively comprehensive introduction to HRS and how it can be applied to crop monitoring; 2) Current state-of-the-art techniques are summarized and analyzed to inform further advances in crop monitoring; 3) Opportunities and challenges for crop monitoring applications using HRS are discussed, and future research is summarized. Finally, through a comprehensive discussion and analysis, the article proposes new directions for using HRS to study crop characteristics, such as new data mining techniques including deep learning provide opportunities for efficient processing of large amounts of HRS data; combining the temporal and dynamic characteristics of crop parameters and vegetation growth processes will greatly improve the accuracy of crop parameter detection and monitoring; multidata fusion and multiscale data assimilation will become HRS monitoring. Multidata fusion and multiscale data assimilation will become another research hotspot for HRS monitoring of crop parameters.

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“…The colors which are reflected by an object is espy as its color which is termed as spectral response. Spectral responses are used to detect ink mismatch in writing [1]- [5], estimation of vegetables biomass [6], crop residues [7], grasslands at high altitudes [8] and vegetation water content [9], monitoring of nitrogen in wheat crop [10], diagnosing the condition of drinking water [11], drill core mineral mapping along with mineralogical data fusion [12], optimal sampling of field for targeted minerals [13], document forensic and forgery detection [3], [5], [14]- [17]. Different approaches are used by scientists to ensure the originality of the document like Thin Layer Chromatography (TLC) which is destructive technique.…”

Section: Introductionmentioning

confidence: 99%