loading

Project Description

Overview

The interactions between various soil microorganisms are challenging to study and thus remain unknown. A major issue in the field of Agriculture is the inability to collect essential data, regarding soil quality for cultivation purposes in large areas. Precision Agriculture is an innovative farming management concept, combined with Data Analysis and AI/Machine Learning models, which aims to revolutionize agriculture and soil improvement, by increasing crop yield and preserving natural resources. The goal of PAGGAIA - Precision AGriculture, using Genomics, Artificial Intelligence and Aero-transportable equipment - is to make a sustainable and positive environmental impact, by optimizing fertilizers' use, seeking quantity and quality yield increase, and modernizing agricultural practices, in a bio-sustainable and consumer-friendly manner. This is accomplished by creating a protocol that includes genomic, physicochemical, and agronomic characteristics, using soil-sampling drones, portable DNA sequencing, and innovative analysis through the advancements of Machine Learning to propose tailor-made interventions to farmers and specialists.

1) Project Inspiration

Our journey in the iGEM competition began with enthusiasm, but also required a plan. Following the iGEM Cycle, we started brainstorming to find feasible ideas in order for our project to result in a useful contribution to society! The idea about this year's project was the continuation of the portable lab equipment from the two previous iGEM Patras' projects, 2020 and 2021. In the spirit of the iGEM competition, we set our minds free and we discussed a variety of topics related to local problems and could possibly involve the mobile laboratory implementation. Thus, we reached out to our primary PI and we came up with a creative project! This year, we are dealing with the agri-food sector, which is traditionally one of the biggest sectors of our country's economy. The creative aspect of developing our project was supported by our secondary PI, with expertise in aeronautical engineering. Our team decided to work on the combination of the portable laboratory and aero-transportable equipment, and their integration into a project dedicated to the improvement of agricultural practices.

2) Project Description

i. Background

Following the iGEM competition motto "Local people solve local problems" we decided to focus on the agricultural sector, which is of great importance to our country's economy. The primary sector employs 12.4% of the country's total labor force in Greece, approximately 600,000 employers1. The agricultural sector in Greece accounts for €6.67 billion and contributes 4.3% to the total Gross Value Added (GVA) which is double compared to the average European one (1.6%)2.

ii. Problems

A major issue in the field of Agriculture is the excessive use of pesticides and fertilizers, which is observed, in an attempt to increase the nutrients of the soil3 and which can also lead to low efficiency of the fertilizers used4. This can also lead to adverse effects on both the environment and human health, since the majority of nitrogenous fertilizers can not be fully absorbed and therefore the nitrate leaching phenomenon is observed resulting in pollution of the underground and surface water5. Also, it is of great importance to gather data regarding soil's physical and chemical properties in large and isolated areas, for the best possible agricultural practice6.

Therefore, after reviewing the existing literature, we decided on the combination between Precision Agriculture tools and other technological advancements, in order to develop a bio-sustainable and innovative way to deal with these problems.

iii. Current approaches and existing knowledge

Precision Agriculture

Precision Agriculture is an innovative concept, aiming to revolutionize agriculture, by increasing and improving the quality of agricultural production, while preserving natural resources and reducing the negative impact on the environment78.

In recent years, the practice of Precision Agriculture has been enabled by the advent of the Global Positioning System (GPS) that facilitates the farmers' ability to locate their precise position in the field. This system allows the mapping of the spatial variability of soil attributes, such as crop yield, terrain/topography features, soil organic matter, electrical conductivity, moisture levels, nitrogen levels, pH and minerals9.

Drones

Precision Agriculture has also incorporated the use of Unmanned Aerial Vehicles / Drones, that can be easily operated by novice pilots10. These drones can be equipped with cameras and other features and therefore can provide additional features and geographic data, also known as geodata. Integrating drones in agricultural practices facilitates better farm analysis, and better agricultural yield1112.

Metagenomics

Metagenomics refers to direct genetic analysis of genomes obtained from different environments. Metagenomics is used to detect all microorganisms that are present in complex environmental samples and is therefore suitable for the genetic analysis of microbial communities, providing better taxonomic resolution and genomic information13.

Machine Learning Models

Machine Learning (ML) is a field of computer science that uses computers to learn from data. It is a subfield of Artificial Intelligence, a field of computer science that tries to simulate human intelligence processes by using computer systems14. Because of the increasing available computational power, Machine Learning models can be used in various fields of research, including agriculture15.

iv. Solution

Basis of the project

The goal of the project is the improvement of soil characteristics, which could help with the quality of the agricultural yield. This will be achieved through a protocol that includes metagenomic data of the bacteria found in the soil samples, physicochemical characteristics of the soil, and agronomic characteristics of the plant. The data will be analyzed through Artificial Intelligence and more specifically a Machine Learning model, in order to propose tailor-made interventions to farmers and specialists in the field of agriculture.

More precisely, the project involves the utilization of state of the art Precision Agriculture technologies, as it integrates the use of GPS technology and unmanned aircraft vehicles (Drones), for the collection of soil samples. The soil sampling for the purpose of our experiment was done manually, from an experimental field with tomato plants, where the soil was previously impregnated with Plant Growth Promoting Bacteria. Metagenomic data regarding the bacterial communities found in the samples, are used for the creation of the dataset for our algorithm.

In Figure 1 we present to you the experiment pipeline of our project, on which you can find more information here!

Figure 1.PAGGAIA project experiment pipeline.

The plant model

The plant model for project PAGGAIA was Solanum lycopersicum L., since tomato is one of the most highly consumed vegetables and constitutes a very basic ingredient of raw, cooked or processed foods16. It is regarded as one of the main crops in the Greek agriculture sector, because of its high yields per hectare17. It is also important to note that tomatoes are highly consumed in Greece, as they are an important component of the Mediterranean diet18. Statistics from FAOSTAT (Food and Agriculture Organization Corporate Statistical Database) for 2014, demonstrate that Greece was included in the top 20 countries worldwide for tomato production, while data from EUROSTAT for 2018, rank Greece 7th in Europe. It is also important to note that tomatoes are a high source of lycopene, which shows important antioxidant activity and therefore it seems to be linked to potential health benefits19.

The selected bacterium

Plant Growth Promoting Bacteria (PGPB) are used as biostimulants, to help promote the growth, yield and quality of crops. Bacillus subtilis is predominantly found in soil and especially in the rhizosphere area and it is one of the most studied PGPB. It can promote plant growth using both direct and indirect mechanisms, including increasing availability of nutrients, the production of antimicrobials, the modulation of plant hormone levels and the improvement of the antioxidant system2021.

The bacteria that grow in the rhizosphere, including B. subtilis, alter trace elements and nutrients substances or mobilize them so they are accessible for plants22. Firstly, it is well known that atmospheric nitrogen is fixed by B. subtilis and also the bacillus promotes the production of it by other bacteria and improves the colonization of other native symbiotic rhizobacteria23. In addition, this bacillus produces various organic acids that solubilize phosphorus into an accessible form for plants24. It is shown from other studies2526 that B. subtilis can cause an upregulation in plant genes that are responsible for iron acquisition, and it increases the mobility of the element due to the acidification of the rhizosphere, resulting in higher levels of iron in plants.

B. subtilis can also help in decreasing the damage that is caused due to drought and salt stress and generally improving resistance in those conditions27. In addition, B. subtilis can alter the regulation of plant growth hormones, compounds that are responsible for growth and development in plants. More specifically, it is either able to produce these compounds or to induce the production of them in plants by the secretion of other substances28.

B. subtilis can also increase the photosynthetic capacity of plants2930, dealing with water deficiency, which causes a decline in photosynthesis30. More specifically, the use of Bacillus subtilis, as a biostimulant, has shown previously that it can improve the growth, the physiology of the plant, the yield and the quality of industrial tomato31. This study has indicated that the application of Bacillus subtilis, as well as B. amyloliquefaciens, significantly increased the mean fruit weight, the antioxidant activity and the quality of industrial tomato.

B. subtilis application increased the carotenoids and lycopene content. The antioxidant activity of tomatoes is based on lycopene32, which is the major carotenoid compound in tomatoes and gives them their characteristic red color33.

B. subtilis and B. amyloliquefaciens increased PME activity, while B. mojavensis recorded the highest increase31. Pectinmethylesterase (PME) and polygalacturonase (PG) are endogenous pectinolytic enzymes in tomatoes related to texture and consistency of juices. The activity control of those is necessary for the determination of the quality in concentrated tomato products. B. subtilis application increased PME and PG activity, total carotenoids and phenolic compounds, as well as lycopene and antioxidant activity.

In other studies, it has been confirmed that Bacillus subtilis strain OSU-142 has the potential to increase the yield, growth, and nutrition of apple trees34. Also, a study has shown that B. subtilis CBR05 can increase the yield and the quality of tomato fruits produced under greenhouse conditions35. In addition, the use of strain BS21-1 has also been tested for its effect on tomato plant growth and other vegetable crops. This strain increased the tomato's plant height as well as leaf width in both organic soil and seed bed soil conditions36.

For the purposes of our project, we have used certain strains of Bacillus subtilis, Bacillus mojavensis, Bacillus amyloliquefaciens and Bacillus thuringiensis, which are permitted in iGEM. Due to biocontainment issues we did not use GMOs in the experimental field.

The Soil Analysis Procedure

  • Next Generation Sequencing

    Next Generation Sequencing (NGS), allows rapid, high-throughput sequencing of nucleic acids. More specifically, with the use of such techniques, it is possible to characterize microbes that are found in low abundance or that cannot be grown with the use of traditional culturing methods37. In addition to this advantage, with NGS it is possible to perform parallel sequencing of different samples. Therefore, with the application of this technology, the bacterial composition of the soil will be identified38.

    For the purposes of the metagenomic analysis of our project, we used 16S rRNA sequencing, on which you can find more details in the guide we created, here!

  • Portable Sequencing

    Nowadays, Third-generation sequencing (TGS) also known as long-read sequencing (LRS), is able to generate reads with much longer lengths in comparison to short-read sequencing39. Specifically, LRS is capable of generating reads of more than 10,000 base pairs and up to hundreds of thousands bases, whereas short-read sequencing (SRS) systems generate reads only a few hundreds, up to around 600bs40. Some benefits of LRS technology are; genome assembly is made easier in comparison to SRS technologies, the detection of variants and the phasing of SNPs into haplotypes, but also the real-time sequencing, the high speed, the portability of true LRS sequencers, real time sequencing and the ability to read RNA sequences directly41.

    Long read sequencing can be performed with a portable sequencing device, which can operate anywhere, even at the sample source, which in this case is the crop field42. An example of such a portable device is the MinION™ (Oxford Nanopore Technologies), which is suitable for real time applications. This device was used during last year's project and this time we are presenting a different application regarding site sequencing at the agricultural field. Due to its simple workflow, it can provide immediate data streaming for rapid, actionable results. Furthermore, this type of device can generate short to ultra-long reads for ultimate experimental flexibility and also has low equipment cost. Additionally, the real time data streaming that this type of device provides, allows to stop sequencing, when the wanted data is obtained, while the on-site analysis approach also minimizes the possibility of sample degradation43.

    Figure 2.MinION™ portable sequencing device43.

  • Soil-Sampling Drone

    This process can also be automated by using aerotransportable equipment, such as specially designed soil-sampling drones. For this purpose, a custom-made soil-sampling mechanism, attachable to a DJI Phantom 3 Drone, was designed according to the report of Blake Rolfing et al.. The soil sampling mechanism is based on a single-acting, spring-return pneumatic piston.

    Figure 3.Drone with custom-made soil-sampling mechanism.
Hardware

With agricultural research advancing in the last decades, comes an exponential need for collecting soil samples from vast cultivated areas. Advances in fabrication, navigation, remote control capabilities, and power storage systems have made possible the development of a wide range of drones that can be utilized in various situations. Depending on the flight missions of the drones, the size and type of installed equipment are different44.

Drones can be categorized in two categories: Fixed Wing Airplanes and Rotary Motor Helicopters. Each of these drones has its own advantages and limitations. The fixed wing drones can fly at higher speeds ranging from 25-45 mph and can cover a range of 500 to 750 acres per hour depending on the battery. Rotary motor drones on the other hand can hover and focus on specific problems in the real world and can fly over constant speed. They suffer from limited battery life and can take off and land off safely in small confined areas and are absolutely best for starters to learn Drone Flying45.

Today's farming operations look nothing like they did even a few decades ago. Today's producers can optimize every aspect of their operations, from field spraying to grow cycles and crop health, thanks to advances in technology. Drones and other forms of unmanned aerial vehicles have played a significant role in this transition (UAV). Farmers benefit from in-depth data analysis and mission planning, as well as technological advances capable of managing physical labor, when employing an agriculture drone.

Remote sensing technology, which takes up radiation on the ground and can monitor everything from physical qualities to the amount of heat an area generates, is one of the keys to this entire process. Moreover, smart spraying and seeding aren't the only approaches to improve agricultural efficiency, reduce costs, and enhance yields. Drones may also be used to map out a region and generate novel insights, eliminating much of the guesswork from the growing process. The finest farm mapping drones take this notion a step further with multispectral imaging. This implies they can detect visible and invisible light sensors within a certain range. This sort of agricultural drone may provide two distinct types of maps: RGB maps and NDVI maps.

Air, water, and soil sampling is an important activity in many sectors and applications. The capacity to gather samples quickly, securely, and reliably is vital for everything from agriculture to wildfire control, air quality studies to oil spills, ecological monitoring to military applications. Currently, this usually entails a human traveling to the site and manually collecting a sample. This procedure consumes time, travel funds, and expertise that may be better spent elsewhere. Sometimes sampling must be done at remote and large areas, or at a specific frequency, which adds to the difficulty of the operation.

This approach suggests a viable solution to this problem: a device that can be attached to a medium-sized drone and gather soil samples autonomously. At this moment, It can collect 8 gram soil samples, allowing it to be used in a wide range of applications. The mechanism is mechanically strong enough to endure the rigors of hostile conditions and must be lightweight to optimize drone flying time and sturdy while landing on solid ground.

The drone used for our project is constructed with the agriculture sector in mind. In order to facilitate soil sampling we used compressed carbon dioxide to force a soil-sampling probe into soil to obtain a sample and a spring to retract the probe before a flight46. More specifically, the soil sampling mechanism that attaches to the drone is based on a single-acting, spring-return pneumatic piston. The system can be summarized in Figure 4.

Figure 4.Pneumatic system schematic of the soil sampling mechanism46.

The 16-gram CO2 cartridge serves as the pressure reservoir, and the solenoid valve, that is normally closed, controls the activation of the plunger, which is a hollow soil sampling probe. When triggered by a microcontroller, the solenoid valve opens, and the system gets pressurized, forcing the plunger into the soil. Due to the friction, the soil sample in the hollow probe is retrained, and therefore pulled out of the soil and safely stowed in the probe for transport. This whole mechanism is detachable and can be adjusted in order to fit custom-made drones. That allows us to take into consideration the aerodynamic design of the drone, the weight of the samples, the contamination of samples and other variables. We would also like to mention that the plunger and top & bottom caps of the design are 3D printed, therefore allowing us to make even more adjustments.

For the final product in order to minimize the device's weight and align its center of mass with that of the drone, we concluded that the mechanism should attach via snap-fit brackets to a unibody base plate with roughly the same footprint as the drone's battery compartment. By doing this we eliminate the extra weight of a full enclosure and allow for secure attachment to the drone. The base plate's unibody construction allows for maximum strength and rigidity with minimum weight. A clamp which holds the soil sampling mechanism vertically must be resistant to withstand the forces of the pneumatically-actuated soil probe colliding with the ground. Having this clamp integrated with the base plate which is directly in contact with the drone's stiff aluminum battery compartment lends extra rigidity to the base plate, allowing some of these forces to disappear into the drone's body rather than the base plate alone, as the latter case could compromise the mechanical integrity of the lightweight, plastic plate.

Final Design

The conceptual design of the soil sample mechanism was developed utilizing off-the-shelf parts as well as 3D-printed components. To eliminate the delays associated with manufacturing a component, the soil sampling probe, base and other components were created utilizing a 3D printed jib and hand tools. Figure 5 shows a section view of the as-built CATIA model of the soil sampler (full attachable device on the left and the soil sampler with the piston on the right).

Figure 5.Schematic representation of the soil sampler attachable device.

Data Analysis and Machine Learning

The development of a Machine Learning model that makes suggestions, was based on weather data, soil data from the fields, quality characteristics and historical yield data. This involves training a model with a dataset and then processing data to make predictions.

3) Innovation

Specially Designed Soil-Sampling Drones

This enables the automation of sample collection with the use of a custom-made soil sampling mechanism and furthermore the accessibility in large and remote areas, enabling the collection of soil samples. Therefore, this allows a wide application of the project and prompts access to agricultural data, in even more areas.

Portable Sequencing Device

A portable sequencing device can be easily transported and used by a single person in the field. Thus, real-time sequencing can be conducted at a sample source supporting rapid species identification.

The algorithm

Through our ML Model, suggestions can be made for any cultivation area of interest, if agronomic, physicochemical and metagenomic data is provided. Therefore, it will be able to give advice regarding soil improvement and crops for planting, through the suggestion of personalized interventions.

References
  1. Hellenic Republic. Facts and Figures (Food and Gastronomy)
    Full text
  2. Klonaris, S. (2021). The Agri-food Sector in Greece: Prospects and Possibilities. In Modeling Economic Growth in Contemporary Greece (pp. 221-244). Emerald Publishing Limited.
    Full text
  3. Savci, S. (2012). Investigation of effect of chemical fertilizers on environment. Apcbee Procedia, 1, 287-292.
    Full text
  4. Fan, Z., Lin, S., Zhang, X., Jiang, Z., Yang, K., Jian, D., ... & Wang, J. (2014). Conventional flooding irrigation causes an overuse of nitrogen fertilizer and low nitrogen use efficiency in intensively used solar greenhouse vegetable production. Agricultural Water Management, 144, 11-19.
    Full text
  5. Ahmed, M., Rauf, M., Mukhtar, Z., & Saeed, N. A. (2017). Excessive use of nitrogenous fertilizers: an unawareness causing serious threats to environment and human health. Environmental Science and Pollution Research, 24(35), 26983-26987.
    Full text
  6. Elnaggar, A. A., & Noller, J. S. (2009). Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sensing, 2(1), 151-165.
    Full text
  7. Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. Advances in agronomy, 67, 1-85.
    Full text
  8. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and electronics in agriculture, 36(2-3), 113-132.
    Full text
  9. Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of agricultural engineering research, 76(3), 267-275.
    Full text
  10. Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926.
    Full text
  11. Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518.
    Full text
  12. Ren, Q., Zhang, R., Cai, W., Sun, X., & Cao, L. (2020, February). Application and development of new drones in agriculture. In IOP Conference Series: Earth and Environmental Science (Vol. 440, No. 5, p. 052041). IOP Publishing.
    Full text
  13. Bharti, R., & Grimm, D. G. (2021). Current challenges and best-practice protocols for microbiome analysis. Briefings in bioinformatics, 22(1), 178-193.
    Full text
  14. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
    Full text
  15. Qaddoum, K., Hines, E.L., Iliescu, D.D., 2013. Yield Prediction for Tomato Greenhouse Using EFuNN. ISRN Artif. Intell. 2013, 1-9.
    Full text
  16. Migliori, C. A., Salvati, L., Di Cesare, L. F., Scalzo, R. L., & Parisi, M. (2017). Effects of preharvest applications of natural antimicrobial products on tomato fruit decay and quality during long-term storage. Scientia Horticulturae, 222, 193-202.
    Full text
  17. Aggidis, A. Field Tomato—Table, Industrial, Cultivation, Utilization (in Greek); Ziti: Thessaloniki, Greece, 2006.
    Full text
  18. Anastasiadis, F., Apostolidou, I., & Michailidis, A. (2020). Mapping sustainable tomato supply chain in Greece: A framework for research. Foods, 9(5), 539.
    Full text
  19. Arballo, J., Amengual, J., & Erdman Jr, J. W. (2021). Lycopene: A critical review of digestion, absorption, metabolism, and excretion. Antioxidants, 10(3), 342.
    Full text
  20. Parray, J. A., Jan, S., Kamili, A. N., Qadri, R. A., Egamberdieva, D., & Ahmad, P. (2016). Current perspectives on plant growth-promoting rhizobacteria. Journal of Plant growth regulation, 35(3), 877-902.
    Full text
  21. de Lima, B. C., Moro, A. L., Santos, A. C. P., Bonifacio, A., Araujo, A. S. F., & de Araujo, F. F. (2019). Bacillus subtilis ameliorates water stress tolerance in maize and common bean. Journal of Plant Interactions, 14(1), 432-439.
    Full text
  22. Hayat, R., Ali, S., Amara, U., Khalid, R., & Ahmed, I. (2010). Soil beneficial bacteria and their role in plant growth promotion: a review. Annals of microbiology, 60(4), 579-598.
    Full text
  23. Elkoca, E., Kantar, F., & Sahin, F. (2007). Influence of nitrogen fixing and phosphorus solubilizing bacteria on the nodulation, plant growth, and yield of chickpea. Journal of Plant nutrition, 31(1), 157-171.
    Full text
  24. Saeid, A., Prochownik, E., & Dobrowolska-Iwanek, J. (2018). Phosphorus Solubilization by Bacillus Species. Molecules, 23(11), 2897.
    Full text
  25. Freitas, M. A., Medeiros, F. H., Carvalho, S. P., Guilherme, L. R., Teixeira, W. D., Zhang, H., & Paré, P. W. (2015). Augmenting iron accumulation in cassava by the beneficial soil bacterium Bacillus subtilis (GBO3). Frontiers in Plant Science, 6, 596.
    Full text
  26. Zhang, H., Sun, Y., Xie, X., Kim, M. S., Dowd, S. E., & Paré, P. W. (2009). A soil bacterium regulates plant acquisition of iron via deficiency‐inducible mechanisms. The Plant Journal, 58(4), 568-577.
    Full text
  27. Woo, O. G., Kim, H., Kim, J. S., Keum, H. L., Lee, K. C., Sul, W. J., & Lee, J. H. (2020). Bacillus subtilis strain GOT9 confers enhanced tolerance to drought and salt stresses in Arabidopsis thaliana and Brassica campestris. Plant Physiology and Biochemistry, 148, 359-367.
    Full text
  28. Arkhipova, T. N., Veselov, S. U., Melentiev, A. I., Martynenko, E. V., & Kudoyarova, G. R. (2005). Ability of bacterium Bacillus subtilis to produce cytokinins and to influence the growth and endogenous hormone content of lettuce plants. Plant and Soil, 272(1), 201-209.
    Full text
  29. Zhang, H., Xie, X., Kim, M. S., Kornyeyev, D. A., Holaday, S., & Paré, P. W. (2008). Soil bacteria augment Arabidopsis photosynthesis by decreasing glucose sensing and abscisic acid levels in planta. The Plant Journal, 56(2), 264-273.
    Full text
  30. Li, Y., Xu, S., Gao, J., Pan, S., & Wang, G. (2016). Bacillus subtilis-regulation of stomatal movement and instantaneous water use efficiency in Vicia faba. Plant growth regulation, 78(1), 43-55.
    Full text
  31. Katsenios, N., Andreou, V., Sparangis, P., Djordjevic, N., Giannoglou, M., Chanioti, S., ... & Efthimiadou, A. (2021). Evaluation of plant growth promoting bacteria strains on growth, yield and quality of industrial tomato. Microorganisms, 9(10), 2099.
    Full text
  32. Di Mascio, P., Kaiser, S., & Sies, H. (1989). Lycopene as the most efficient biological carotenoid singlet oxygen quencher. Archives of biochemistry and biophysics, 274(2), 532-538.
    Full text
  33. Nguyen, M. L., & Schwartz, S. J. (1999). Lycopene: Chemical chemical and biological properties: Developing nutraceuticals for the new millenium. Food Technology (Chicago), 53(2), 38-45.
    Full text
  34. Pirlak, L., Turan, M., Sahin, F., & Esitken, A. (2007). Floral and foliar application of plant growth promoting rhizobacteria (PGPR) to apples increases yield, growth, and nutrient element contents of leaves. Journal of Sustainable Agriculture, 30(4), 145-155.
    Full text
  35. Chandrasekaran, M., Chun, S. C., Oh, J. W., Paramasivan, M., Saini, R. K., & Sahayarayan, J. J. (2019). Bacillus subtilis CBR05 for tomato (Solanum lycopersicum) fruits in South Korea as a novel plant probiotic bacterium (PPB): Implications from total phenolics, flavonoids, and carotenoids content for fruit quality. Agronomy, 9(12), 838.
    Full text
  36. Lee, S. W., Lee, S. H., Balaraju, K., Park, K. S., Nam, K. W., Park, J. W., & Park, K. (2014). Growth promotion and induced disease suppression of four vegetable crops by a selected plant growth-promoting rhizobacteria (PGPR) strain Bacillus subtilis 21-1 under two different soil conditions. Acta Physiologiae Plantarum, 36(6), 1353-1362.
    Full text
  37. Clooney, A. G., Fouhy, F., Sleator, R. D., O'Driscoll, A., Stanton, C., Cotter, P. D., & Claesson, M. J. (2016). Comparing apples and oranges?: next generation sequencing and its impact on microbiome analysis. PloS one, 11(2), e0148028.
    Full text
  38. Wooley, J.C., Ye, Y. Metagenomics: Facts and Artifacts, and Computational Challenges. J. Comput. Sci. Technol. 25, 71-81 (2010).
    Full text
  39. Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome biology, 21(1), 1-16.
    Full text
  40. Adewale B. A. (2020). Will long-read sequencing technologies replace short-read sequencing technologies in the next 10 years?. African journal of laboratory medicine, 9(1), 1340.
    Full text
  41. Jain, M., Koren, S., Miga, K. H., Quick, J., Rand, A. C., Sasani, T. A., Tyson, J. R., Beggs, A. D., Dilthey, A. T., Fiddes, I. T., Malla, S., Marriott, H., Nieto, T., O'Grady, J., Olsen, H. E., Pedersen, B. S., Rhie, A., Richardson, H., Quinlan, A. R., Snutch, T. P., … Loose, M. (2018). Nanopore sequencing and assembly of a human genome with ultra-long reads. Nature biotechnology, 36(4), 338-345.
    Full text
  42. Brown BL, Watson M, Minot SS, Rivera MC, Franklin RB. MinION™ nanopore sequencing of environmental metagenomes: a synthetic approach. Gigascience. 2017 Mar 1;6(3):1-10.
    Full text
  43. Lu, H., Giordano, F., & Ning, Z. (2016). Oxford Nanopore MinION sequencing and genome assembly. Genomics, proteomics & bioinformatics, 14(5), 265-279.
    Full text
  44. Hassanalian, M., & Abdelkefi, A. (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91, 99-131.
    Full text
  45. Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518.
    Full text
  46. Report: Autonomous Drone for Air, Water, and Soil Sampling by Colby Smith, Chantel Lapins, Issak Allaire-MacDonald, Rudy Gapinski, Blake Rolfing, Ryan Dalby Advisor: Dr. Kam K. Leang, University of Utah, department of Mechanical Engineering.
    Full text