272 research outputs found
Robots in Agriculture: State of Art and Practical Experiences
The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming
Drone Technology in Precision Agriculture: Are There No Environmental Concerns?
The adoption of drones in precision agriculture is expanding at a rapid rate, and expected to rise even faster as improvements in the technology result in cheaper models. Studies on the economic impact of drone technology in precision agriculture present optimistic projections of increased global food production. But increased food production almost always comes with significant environmental concerns. This paper examines the environmental concerns of drone technology in precision agriculture. The methodology of this paper is theoretical analysis and extrapolation of current literature in order to reveal the gap which future research needs to fill. While proposing a new area that has not received the close attention of experts and researchers, the paper reveals future scenarios of environmental issues around the various methods of drone applications in agricultural practices. Keywords: Drone technology, precision agriculture, agricultural practices, environmental impact, food security words DOI: 10.7176/JEES/10-9-08 Publication date:September 30th 202
INTELLIGENT WIRELESS TECHNOLOGY USAGE EFFECT IN CONTEXT OF PHYTOSANITARY TREATMENT SPRAYING
In agriculture, pesticides and fertilizers are applied to prevent crop disease and increase plant productivity. As a result of the digitalization of agriculture, human labor is increasingly interacting with intelligent technology through robots to facilitate agricultural operations. The use of intelligent technology protects the natural ecosystem by reducing the major damage caused by the unconventional application of phytosanitary treatments resulting in a flexible, proportional spraying at precise angles, thus avoiding the generation of large amounts of chemicals. This paper presents a short review about the state of the art of wireless sensors networks and how together with robotics can be applied in different fields of agriculture through the prism of sprayers that include a detection system and a wireless controlled spraye
Design and development of an unmanned aerial and ground vehicles for precision pesticide spraying
Günümüzde, bitki hastalıkları tarımsal üretimi etkileyen önemli sorunlardan birisi olarak karşımıza çıkmaktadır. Bitkileri hastalıklardan ve zararlı otların etkilerinden korumak hem tarımda üretimi artırmak hem de tarımın kalitesini yükseltmek için büyük önem taşımaktadır. Tarımsal ürünler, ülkemizde ve dünyada çeşitli ilaçlama yöntemleri kullanılarak korunabilmektedir. Bu yöntemlerin başında gelen ilaçlama yolu ile bitki koruma yöntemi üretimin kalitesini geliştirmek ve rekolteyi artırmak amacıyla yaygın olarak kullanılmaktadır. Ancak bitkilerin korunmasında uygulanan geleneksel ilaçlama yöntemlerinin bitkilere ve toprağa büyük ölçüde zarar verdiği gözlenmektedir. Son yıllarda gelişmiş ülkelerdeki tarımsal uygulamalarda robotların kullanımı hızla artmakta, tarımsal alanlarda özellikle uzaktan algılama ve hassas tarım çalışmalarında bu robotların kullanıldığı görülmektedir. Dahası, tarımsal üretimde yararlanılan fayda-maliyet oranı da dikkate alındığında, günümüzde hassas tarım uygulamalarında robotların kullanılmasının kaçınılmaz hale geldiği anlaşılmaktadır. Günümüz gereksinimleri ve gelişen teknoloji göz önüne alınarak planlanmış olan bu çalışmada, ülkemizde yaygın olarak kullanılan tarımsal mücadele yöntemlerinin maliyetlerini, tarımsal üretimin miktarını ve kalitesini önemli ölçüde etkileyecek geleneksel ilaçlama yöntemlerine alternatif olabilecek bir tarımsal mücadele sistemi geliştirilmiştir. Çalışmada, yakın mesafeden doğrudan hedeflenen bitki üzerine ilaçlama yapılması, ilaçlama sırasında toprağa ve bitkilere verilen zararın en aza indirgenmesi hedeflenmiştir. Bu doğrultuda, özgün tasarım multispektral kamera, ilaçlama ünitesi, Yer Kontrol İstasyonu (YKİ) ve eşgüdümlü çalışabilen İnsansız Hava Aracı (İHA) ile İnsansız Yer Aracından (İYA) oluşan tarımsal mücadele mekanizması tasarlanmış ve geliştirilmiştir. Bu mekanizma, tarımsal ilaçlama uygulamaları için geleneksel yöntemlere kıyasla daha ileri düzey bir alternatif yöntem olarak ortaya çıkmaktadır.TABLE OF CONTENTS
ÖZET ................................................................................................................ vii
ABSTRACT ....................................................................................................... ix
ACKNOWLEDGEMENTS ................................................................................ xi
1 . INTRODUCTION .......................................................................................... 1
2. LITERATURE REVIEW ............................................................................. 6
2.1 Robotics ..................................................................................................... 9
2.2 Unmanned Ground Vehicles ..................................................................... 11
2.3 Unmanned Aerial Vehicles ....................................................................... 11
2.4 Remote Sensing Technology .................................................................... 17
2.4.1 Remote Sensing Platforms ................................................................. 19
2.4.2 Plant Disease Detection ..................................................................... 22
2.4.3 Normalized Difference Vegetation Index ........................................... 27
3 . MATERIAL AND METHOD ....................................................................... 29
3.1 Ground Control Station ............................................................................ 32
3.2 Unmanned Ground Vehicle ...................................................................... 37
3.2.1 Specifications of the UGV ................................................................. 38
3.2.2 The Chassis and Sensor Holder .......................................................... 40
3.2.3 FEM Analysis .................................................................................... 43
3.3 Multispectral Camera for Plant Disease Detection .................................... 44
3.3.1 Spectral Imaging ................................................................................ 46
3.3.2 Multispectral Camera – Spektra TSL128RN ...................................... 47
3.3.3 The hardware of the Device ............................................................... 49
3.3.4 Calibrating Steps of the Device .......................................................... 52
3.3.5 Software for the Device ..................................................................... 56
3.3.6 Measurements using NDVI Devices .................................................. 58
3.4 Unmanned Aerial Vehicle ........................................................................ 62
3.4.1 The Chassis and Arm ......................................................................... 66
3.4.2 FEM Analysis ................................................................................... 69
3.4.3 Modal Analysis ................................................................................. 70
3.4.4 Performance of the Propellers ............................................................ 73
3.4.5 Flight Duration and Maximum Conditions ......................................... 82
3.4.6 Strain Measurement ........................................................................... 84
3.4.7 Other Parts ........................................................................................ 92
3.4.8 Specifications of the UAV ................................................................. 95
3.4.9 Flight Tests ....................................................................................... 96
3.5 Spraying Unit –Sprayer and Tank ............................................................. 99
4 . RESULTS AND DISCUSSION .................................................................. 103
4.1 The UGV ............................................................................................... 103
4.2 The Multispectral Camera ...................................................................... 105
4.3 The UAV ............................................................................................... 115
4.4 The Sprayer............................................................................................ 135
xv
4.5 UGV and Multispectral Camera .............................................................. 138
4.6 Aerial Spraying UAV ............................................................................. 145
5 . CONCLUSIONS......................................................................................... 154
REFERENCES ................................................................................................ 156
RESUME......................................................................................................... 16
Design of Plant Protection UAV Variable Spray System Based on Neural Networks
Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized
Design, Prototype Manufacturing and Performance of a Drone for Vineyard Spraying
The application of pesticides in vinyard areas is of crucial importance for grape yields. Field sprayers and atomizers are commonly used for pesticide applications in vinyards. The aim of this research is to develop a drone that will be an alternative to ground vehicles, to expand its use, to reduce the use of pesticides, as well as safer production with less pesticides in the environment, in vinyards area. In accordance to this purpose, a drone (unmanned aerial vehicle) with 6 motors and a multi-copter system (Hexacopter) was designed and prototype manufactured by using open source software program.
The flight tests were carried out in the vineyard areas of Dicle University. In the experiments, water sensitive papers and filter papers were used to measure the amount of trace substance deposite rate and coverage rate. These papers were placed in the upper, middle and lower parts of the vine before started of the tests. Spraying experiments were then carried out at 0.5 ms-1, 1.00 ms-1 and 2 ms-1flight speeds and at different flight altitude such as 30 cm, 60 cm and 90 cm and different part of vine as upper, middle and lower part. Each test was carried out triplicated.
According to results, spray deposition and coverage rates were found to decrease with increased flight speed of drone and flight altitudes. At all flight speeds and altitudes, the highest amount of deposite and coverage rate were found in the upper part of the vine, while this ratio decreased towards the lower region. The increase in the spray altitude was negatively affected the penetration of the droplets into the plant. In general, the best amount of trace material deposite and coverage rate were was obtained at 0.5 ms-1 flight speed of drone, 30 cm flight altitude and upper section of vine. While the amount of deposite in the plant at 0.5 ms-1 flight speed was obtained 19.61 µgcm-2, this value decreased to 11.21 µgcm-2 at 60 cm altitude and 6.05 µgcm-2 at 90 cm flight altitude. As a result, we can argued that droplet distribution will be more homogeneous, droplet deposition effect well, and environmental pollution will be reduced, in the application of the remote-control drone and low ltitude sprayin, it also will play a very important role in the vinyard pest control.
 
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Drones: Innovative Technology for Use in Precision Pest Management.
Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers
Design of Plant Protection UAV Variable Spray System Based on Neural Networks
Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized
Autonomous surveillance for biosecurity
The global movement of people and goods has increased the risk of biosecurity
threats and their potential to incur large economic, social, and environmental
costs. Conventional manual biosecurity surveillance methods are limited by
their scalability in space and time. This article focuses on autonomous
surveillance systems, comprising sensor networks, robots, and intelligent
algorithms, and their applicability to biosecurity threats. We discuss the
spatial and temporal attributes of autonomous surveillance technologies and map
them to three broad categories of biosecurity threat: (i) vector-borne
diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a
broad range of opportunities to serve biosecurity needs through autonomous
surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799,
http://dx.doi.org/10.1016/j.tibtech.2015.01.003.
(http://www.sciencedirect.com/science/article/pii/S0167779915000190
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