Image Analysis Based on Heterogeneous Architectures for Precision Agriculture: A Systematic Literature Review

Pusdá-Chulde, Marco; Salazar-Fierro, Fausto A.; Sandoval-Pillajo, Ana Lucía; Herrera-Granda, Erick P.; García-Santillán, Iván; Giusti, Armando Eduardo De

doi:10.1007/978-3-030-33614-1_4

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…However, despite the several advantages of A4.0 and precision techniques, few reviews of the literature have been carried out on PA and A4.0, such as the work of Pusdá-Chulde et al [29] who reviewed heterogeneous architectures for PA, and Klerkx et al [30] who performed the review of social science on digital agriculture. Despite these comprehensive studies, no literature or bibliometric reviews have been carried out to review empirical results and understand the impact of technological adoption as well as the main challenges and trends for the coffee sector.…”

Section: Introductionmentioning

confidence: 99%

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.

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Section: Introductionmentioning

confidence: 99%

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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“…Like other systematic literature and scoping reviews [17][18][19][20], and following the PRISMA guidelines [21], the review methodology has focused on databases, to which the authors have access, such as SpringerLink, IEEEXplore, MDPI (Institute for Multidisciplinary Digital Publication), Scopus, and ACM (Association for Computing Machinery), and consisted of four steps: (i) research questions, (ii) document search, (iii) paper selection, and (iv) data extraction.…”

Section: Methodsmentioning

confidence: 99%

A Scoping Review on Virtual Reality-Based Industrial Training

et al. 2020

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The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. The methodology consisted of four steps, which pose research questions, document search, paper selection, and data extraction. From a total of 350 peer-reviewed database articles, such as SpringerLink, IEEEXplore, MDPI, Scopus, and ACM, 44 were eventually chosen, mostly using the virtual reality haptic glasses and controls from Oculus Rift and HTC VIVE. It was concluded that, among the advantages of using this digital tool in the industry, is the commitment, speed, measurability, preservation of the integrity of the workers, customization, and cost reduction. Even though several research gaps were found, virtual reality is presented as a present and future alternative for the efficient training of human resources in the industrial field.

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“…At present, field-based sensors are widely adopted for automatic tree identification, such as Visible-Near-Infrared imaging [10,11], stereo imaging [12,13] and thermal imaging [14,15]. Target extraction based on RGB (Red Green Blue) digital cameras has seen a wide application in precision farming due to its low cost and non-contact data collecting [16][17][18]. In this process, color index-based segmentation techniques are mostly applied to complete the crucial background removal.…”

Section: Introductionmentioning

confidence: 99%

Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds

Cheng

2021

Agriculture

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Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vital for precision pesticide application. However, the extant studies do not provide an efficient and reliable method of extracting individual trees with irregular tree-crown shapes and complicated backgrounds. This paper on our study proposes a Mahalanobis distance and conditional random field (CRF)-based segmentation model to extract cherry trees accurately in a natural orchard environment. This study computed Mahalanobis distance from the image’s color, brightness and location features to acquire an initial classification of the canopy and background. A CRF was then created by using the Mahalanobis distance calculations as unary potential energy and the Gaussian kernel function based on the image color and pixels distance as binary potential energy. Finally, the study completed image segmentation using mean-field approximation. The results show that the proposed method displays a higher accuracy rate than the traditional algorithms K-means and GrabCut algorithms and lower labeling and training costs than the deep learning algorithm DeepLabv3+, with 92.1%, 94.5% and 93.3% of the average P, R and F1-score, respectively. Moreover, experiments on datasets with different overlap conditions and image acquisition times, as well as in different years and seasons, show that this method performs well under complex background conditions, with an average F1-score higher than 87.7%.

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Image Analysis Based on Heterogeneous Architectures for Precision Agriculture: A Systematic Literature Review

Cited by 10 publications

References 43 publications

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

A Scoping Review on Virtual Reality-Based Industrial Training

Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds

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