Towards Smart Farming and Sustainable Agriculture with Drones

Tripicchio, Paolo; Satler, Massimo; Dabisias, Giacomo; Ruffaldi, Emanuele; Avizzano, Carlo Alberto

doi:10.1109/ie.2015.29

Cited by 200 publications

(98 citation statements)

References 7 publications

(7 reference statements)

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“…Hence, a higher degree of information can be obtained because the higher flexibility allows for an extended range of measurement days. Furthermore, UAVs offer time-saving and cheaper image recording, enable flexible and immediate image processing and give a survey of the health of farm systems [4]. In addition to the time-saving and cheaper image processing, the flexible handling options for aim-oriented use, e.g., variable flight height and better image resolution [5,6], UAVs represent new opportunities in the agricultural sciences, especially in precision farming and precision phenotyping.…”

Section: Introductionmentioning

confidence: 99%

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

2017

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Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13-15 (it is a scale to identify the phenological development stage of a plant which is here the 3-to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R 2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.

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Section: Introductionmentioning

confidence: 99%

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

2017

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“…As result of developments in drone technology, the use of these tools for agricultural purposes has become widespread and it has become possible for farmers to access the images they need instantly or by remote control of activities such as pesticide applications at low cost (Malveaux et al 2014;Tripicchio et al). Drones that specially developed for agricultural applications, has sensitive sensors and imaging systems to detect pests and plant diseases (Doyle, van der Wal et al2015).…”

Section: Internet Of Things and Drones In Agriculturementioning

confidence: 99%

Digital agriculture practices in the context of agriculture 4.0

Özdoğan¹,

Gacar²,

Aktaş³

2017

Pressacademia

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Purpose-Agricultural production is under heavy pressure based upon increasing world population and significant changes in the climate. In this study, the concept of digital agriculture practices and their effects on agricultural productivity is discussed. An evaluation of current circumstance is made through the cases of Doktar Inc. and Tarla.io which are digital agriculture companies located in Turkey. Methodology-This study has utilized case method to evaluate the current circumstance of digital agriculture applications in Turkey. Since digital agriculture is an area that is still in early development stage in Turkey, case method is one of the most suitable methods. 11 openended questions and subsequent interviews sent to Doktor Inc and Tarla.io General Managers via e-mail and answers are evaluated with other collected data. Findings-Digital agriculture applications are in the early development stage in Turkey. The companies that are discussed in the paper have made meaningful progress regarding raising awareness of farmers and other involved parts of agriculture sector in Turkey. While the penetration of the two companies is currently not enough both as volume and quantity, the applications used for digital agriculture by them are parallel with the applications in developed countries. Conclusion-Digital agriculture practices in Turkey have yet to be implemented in very limited, but there are steps to be taken to acceleration. To develop digital farming in Turkey, supports of government have strategic priorities. In this context, the development of a digital agriculture action plan and supporting of this strategy with related policies and implementations, like in the EU countries and USA, will enable the expansion of agricultural production vision in Turkey. Technopolis and incubation centers of universities will be able to transform the accumulated scientific knowledge into initiatives and create a digital agriculture-focused ecosystem.

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“…Information about parasite infestations, humidity and temperature can be effectively used to improve and automate farming processes. In this kind of IoT context, the keywords are Precision Farming and Smart Agriculture, Farming Drones, Ecological prediction models, and many more.…”

Section: Introductionmentioning

confidence: 99%

A predictive Decision Support System (DSS) for a microalgae production plant based on Internet of Things paradigm

Giannino

Esposito²,

Diano³

et al. 2018

Concurrency and Computation

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Summary The production of microalgae represents a large and rapidly expanding market with several applications in the fields of food, pharmaceutics, cosmetics, and energy. Microalgae are photosynthetic aquatic microorganisms whose growth is mainly controlled by a few environmental parameters: temperature, light, pH, and nutrient availability. For this reason, monitoring and controlling such parameters is crucial for their production. At the same time, the development of mathematical models to simulate the behavior of biological systems has become a major predictive and control tool of production processes. In this paper, we present an Internet of Things (IoT) system that can couple sensor data collected directly by biotechnological cultivations with a predictive simulation model. The IoT system constitutes the core of a Decision Support System developed to help the end‐user in the management of industrial production processes.

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Towards Smart Farming and Sustainable Agriculture with Drones

Cited by 200 publications

References 7 publications

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Digital agriculture practices in the context of agriculture 4.0

A predictive Decision Support System (DSS) for a microalgae production plant based on Internet of Things paradigm

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