Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm

Xie, Ziwen; Chen, Song; Gao, Guizhen; Li, Hao; Wu, Xiaoming; Li, Meng; Ma, Yuntao

doi:10.1007/s11119-022-09904-4

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…Whereas association algorithms include apriori and frequent pattern (FP) growth. Supervised algorithms are further grouped into regression and classification models [72]. Regression algorithms constitute logistic regression, linear regression, decision trees (DT), and hierarchical neural networks.…”

Section: Machine Learning Approachmentioning

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: Machine Learning Approachmentioning

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

View full text Add to dashboard Cite

show abstract

“…Random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), naive Bayes (NB), and support vector machines (SVM) are commonly used machine learning algorithms that can be applied to many tasks in plant breeding programs [9,[54][55][56][57]. These methods have been used to estimate the maturity of various crops such as soybean [23], Durian fruits [58], and rapeseed [59].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Bazrafkan,

Navasca,

Kim

et al. 2023

Remote Sensing

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Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas.

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Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes

Gu,

Huang,

Lou

et al. 2024

Computers and Electronics in Agriculture

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Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm

Cited by 5 publications

References 51 publications

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

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

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes

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