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Seiche AT, Wittstruck L, Jarmer T. Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1544. [PMID: 38475081 DOI: 10.3390/s24051544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/14/2024]
Abstract
In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.
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Affiliation(s)
- Anna Teresa Seiche
- Institute of Computer Science, Osnabrück University, 49090 Osnabrück, Germany
| | - Lucas Wittstruck
- Institute of Computer Science, Osnabrück University, 49090 Osnabrück, Germany
| | - Thomas Jarmer
- Institute of Computer Science, Osnabrück University, 49090 Osnabrück, Germany
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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Cacciuttolo C, Guzmán V, Catriñir P, Atencio E, Komarizadehasl S, Lozano-Galant JA. Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining. SENSORS (BASEL, SWITZERLAND) 2023; 23:6846. [PMID: 37571628 PMCID: PMC10422650 DOI: 10.3390/s23156846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.
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Affiliation(s)
- Carlos Cacciuttolo
- Civil Works and Geology Department, Catholic University of Temuco, Temuco 4780000, Chile; (V.G.); (P.C.)
- Department of Civil Engineering, Universidad de Castilla-La Mancha, Av. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain; (E.A.); (J.A.L.-G.)
| | - Valentina Guzmán
- Civil Works and Geology Department, Catholic University of Temuco, Temuco 4780000, Chile; (V.G.); (P.C.)
| | - Patricio Catriñir
- Civil Works and Geology Department, Catholic University of Temuco, Temuco 4780000, Chile; (V.G.); (P.C.)
| | - Edison Atencio
- Department of Civil Engineering, Universidad de Castilla-La Mancha, Av. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain; (E.A.); (J.A.L.-G.)
- School of Civil Engineering, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2340000, Chile
| | - Seyedmilad Komarizadehasl
- Department of Civil and Environment Engineering, Universitat Politècnica de Catalunya, BarcelonaTech, C/Jordi Girona 1-3, 08034 Barcelona, Spain;
| | - Jose Antonio Lozano-Galant
- Department of Civil Engineering, Universidad de Castilla-La Mancha, Av. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain; (E.A.); (J.A.L.-G.)
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Marzougui A, McGee RJ, Van Vleet S, Sankaran S. Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics. FRONTIERS IN PLANT SCIENCE 2023; 14:1111575. [PMID: 37152173 PMCID: PMC10161932 DOI: 10.3389/fpls.2023.1111575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/06/2023] [Indexed: 05/09/2023]
Abstract
Introduction Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model. Methods The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points. Results and discussion The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.
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Affiliation(s)
- Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Rebecca J. McGee
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, United States
| | - Stephen Van Vleet
- Agriculture and Natural Resources, Washington State University Extension, Colfax, WA, United States
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
- *Correspondence: Sindhuja Sankaran,
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Pintér K, Nagy Z. Building a UAV Based System to Acquire High Spatial Resolution Thermal Imagery for Energy Balance Modelling. SENSORS (BASEL, SWITZERLAND) 2022; 22:3251. [PMID: 35590942 PMCID: PMC9101370 DOI: 10.3390/s22093251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
High spatial resolution and geolocation accuracy canopy evapotranspiration (ET) maps are well suited tools for evaluation of small plot field trials. While creating such a map by use of an energy balance model is routinely performed, the acquisition of the necessary imagery at a suitable quality is still challenging. An UAV based thermal/RGB integrated imaging system was built using the RaspberryPi (RPi) microcomputer as a central unit. The imagery served as input to the two-source energy balance model pyTSEB to derive the ET map. The setup's flexibility and modularity are based on the multiple interfaces provided by the RPi and the software development kit (SDK) provided for the thermal camera. The SDK was installed on the RPi and used to trigger cameras, retrieve and store images and geolocation information from an onboard GNSS rover for PPK processing. The system allows acquisition of 8 cm spatial resolution thermal imagery from a 60 m height of flight and less than 7 cm geolocation accuracy of the mosaicked RGB imagery. Modelled latent heat flux data have been validated against latent heat fluxes measured by eddy covariance stations at two locations with RMSE of 75 W/m2 over a two-year study period.
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Affiliation(s)
- Krisztina Pintér
- MTA-MATE Agroecology Research Group, Hungarian University for Agriculture and Life Sciences, Páter K. u. 1., H-2100 Gödöllő, Hungary
| | - Zoltán Nagy
- Department of Plant Physiology and Plant Ecology, Institute of Agronomy, Hungarian University for Agriculture and Life Sciences, Páter K. u. 1., H-2100 Gödöllő, Hungary
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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Banerjee BP, Spangenberg G, Kant S. CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements. BIOSENSORS 2021; 12:bios12010016. [PMID: 35049643 PMCID: PMC8774002 DOI: 10.3390/bios12010016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/25/2021] [Accepted: 12/27/2021] [Indexed: 05/12/2023]
Abstract
The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
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Affiliation(s)
| | - German Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence: ; Tel.: +61-3-4344-3179
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