PRE2022 3 Group11
| Name | Student number | Study |
|---|---|---|
| Vietlinh Pham | 1616420 | Industrial Engineering |
| Lars Nobbe | 1746111 | Applied Mathematics |
| Wilbur van Lierop | ||
| Paul van Geest | 146326 | Electrical Engineering |
| Aloysia Prakso | Industrial Design | |
| Mauritius van Maurik | 1600426 | Automotive Technology |
Problem Statement and Objectives
Some problem statement examples:
The challenge of developing autonomous farming technology that is robust, reliable, and economically feasible for farmers to use. (Building cheap and reliable robotics to support farming)
The need for better methods for evaluating and optimizing the performance of autonomous farming systems, including the use of metrics for yield and efficiency. (More efficency more yield using tech)
Who are the users
1. Farmers
2. Agricultural Businesses
3. Government Agencies
4. Researchers and Scientists
What do they require
Approach, milestones and deliverables
Task Division
SotA: Literature study
Mau:
1.
Article: http://27.109.7.66:8080/xmlui/handle/123456789/682
The article highlights the efforts being made to automate the labor-intensive agriculture industry through the use of robots and machines. A vision-based row guidance method is proposed for autonomous farming robots to navigate through row crops in a field, using machine vision to detect the offset and heading angle in real-time. The robot platform is designed with an open architecture and a control scheme for row guidance. The focus of the robot is to monitor the health of the plants by observing their leaf color and height, as well as the surrounding environmental conditions such as temperature, moisture, and humidity. The information collected is then used to determine the health of the plant, which is displayed on an LCD screen.
2.
Article: https://ieeexplore.ieee.org/abstract/document/9080736
This paper discusses the how wireless sensor network can be used to detect weeds. However, when a lot of static sensors are present the project becomes expensive and chaotic. Therefore the researches decided to make use of autonomous bots which are equipped with ultrasonic sensors and cameras that can detect weeds. The camera input is then processed using a neural network and image segmentation. Once weeds were detected, herbicides were sprayed on them using solenoid valves.
3.
Article: https://link.springer.com/chapter/10.1007/978-90-481-9277-9_20#Abs1
The text discusses the demand for advances in automation in agriculture, horticulture, and forestry due to high labor costs. The focus is on the potential of robotic outdoor systems to increase efficiency and make operations economically viable. The chapter provides examples of autonomous crop protection operations that are likely to be commercially available in the near future. These operations, such as scouting and monitoring, can be automated for increased efficiency, but current systems still have drawbacks, including a lack of robust and safe behaviors. The use of high-precision targeting based on individual weed plant detections holds the potential to greatly reduce the use of resources, such as herbicides.
4.
Article:An overview of smart irrigation systems using IoT - ScienceDirect
The paper provides an overview of the field of agricultural robotics, which has become a popular topic of research and development in recent years. It highlights the critical challenges faced by the agriculture industry, such as labor shortages and the need for environmentally friendly practices, and how agricultural robotics can address these issues. The paper also presents an overview of the current state-of-the-art in agricultural robotics, including individual robots for specific tasks and cooperative teams of robots for farming tasks. The paper concludes by discussing the challenges that still need to be addressed in order to fully automate agricultural production, which is seen as a promising solution for sustaining the growing human population
5.
Article: https://www.mdpi.com/2073-4395/11/9/1818
This paper discusses how agricultural sustainability can be enhanced by integrating technology. Improving of irrigation systems is of importance and IoT and sensory systems could habilitate this. Automated irrigation systems are important for conserving water. IoT and automation are linked to agriculture and farming techniques for making processes more effective and efficient. Moreover, sensory systems improve farmers' understanding of crops and reduce environmental impact and conserve resources.
Vietlinh:
1.
The paper reviews recent advancements in the application of automation and robotics in precision agriculture. The aim of precision agriculture is to maximize agriculture produce while minimizing environmental impact through precise farm management using modern technology. The paper highlights challenges and provides suggestions for the design of efficient autonomous agricultural robotic systems that consider all possibilities and challenges in different types of agriculture operations and take into account development cost to make it affordable for farmers.
2.
Article: https://ieeexplore.ieee.org/document/9243253
This paper reviews three important developments in the field of autonomous robotics in agriculture. These developments include navigation using GPS technology and vision-based navigation, harvesting systems with sensors and actuators, and a soil analysis system to provide farmers with information about the land. The paper presents successful research and applications in these areas and highlights the needs for additional research and development to bring this technology to developing countries where it is not widely used in agriculture.
3.
Article: https://www.sciencedirect.com/science/article/pii/S1110982321000582
This paper focuses on new approaches in smart farming and highlights the importance of data gathering, transmission, storage, and analysis in solving the challenges of food shortage and population growth. IoT is crucial in smart systems, and the smart irrigation systems uses sensors for monitoring water level, irrigation efficiency, and climate. The use of unmanned aerial vehicles (UAVs) and robots in agriculture is also discussed, and their applications include harvesting, seeding, weed detection, irrigation, and pest control. The paper also mentions the role of artificial intelligence (AI), deep learning (DL), machine learning (ML), and wireless communications in smart farming. The paper highlights the challenges in implementing smart farming in developing countries and the need for government and private sector support, as well as the use of Smart Decision Support Systems (SDSS) for real-time analysis and soil mapping to support proper decision making.
4.
Article: https://ieeexplore.ieee.org/abstract/document/9003290
This paper examines the impact of IoT and smart computing technologies on agriculture and farming. It highlights how these technologies have revolutionized the industry and are now widely used for tasks such as monitoring crops and soil moisture. However, the paper also warns of the cybersecurity threats and vulnerabilities that come with using IoT and smart communication technologies in the smart farming environment. The paper provides a holistic study of the security and privacy issues in a smart farming ecosystem, outlining a multi-layered architecture and discussing potential cyber-attack scenarios. The paper also identifies open research challenges and future directions in the field.
5.
Article: https://onlinelibrary.wiley.com/doi/full/10.1002/aepp.13177
The paper describes the potential benefits and regulatory challenges of using autonomous crop equipment in agriculture. The paper aims to summarize the primary regulatory issues related to the use of autonomous equipment for crop production and its impact on the development and adoption of this technology. The paper covers three main objectives:
1. Summarizing the lessons learned from regulation of autonomous equipment in other sectors of the economy
2. Describing the current status of regulation related to autonomous crop equipment
3. Providing an example from the United Kingdom of how regulation can affect the development of autonomous equipment in crop production.
The paper also argues that regulation will have a major impact on the type of autonomous crop equipment that is commercialized and the pattern of its adoption.
Aloysia:
Articles 1-5 can help justify the need for the development of vertical farming (without focusing on vertical farming itself). Articles 6-9 discuss the benefits and drawbacks of vertical farming. Articles 7-12 discusses crops / most efficient input→ output in food/farming.
1: Rundlöf, M., Edlund, M., & Smith, H. G. (2010). Organic farming at local and landscape scales benefits plant diversity. Ecography, Ecography(3), 514–522. https://doi.org/10.1111/j.1600-0587.2009.05938.x
The article examined the effects of organic farming on plant diversity at both local and landscape scales. They hypothesizes that the consequences of organic farming would differ depending on the scale of uptake in a particular landscape. The study utilizes a landscape scale approach and found that the local effect of organic farming was consistently stronger, with higher diversity in borders adjoining organic fields. The results showed that “forb richness” was higher in borders located in landscapes with higher proportion of organic land (potentially due to dispersal of mainly annual plant species from the organic fields). The study highlights the importance of considering multiple scales, including local and landscape factors, to better understand biodiversity patterns, and the potential benefits of organic farming.
2: Lobley, M., Butler, A., & Reed, M. (2009). The contribution of organic farming to rural development: An exploration of the socio-economic linkages of organic and non-organic farms in England. Land Use Policy, 26(3), 723–735. https://doi.org/10.1016/j.landusepol.2008.09.007
The article explores the socio-economic link between organic and non-organic farms, and the contribution of organic farming to rural development (in England). The study found that organic farms tend to employ more people and differ in characteristics (to non-organic farms). Differences in local economic connections between the two were miniscule. The authors argue that a more nuances approach is needed when considering the rural development benefits of organic farming, where other factors such as the type of enterprises on the farm and the marketing strategies adopted by the business should also be taken into account. The focus of debate shifted from equating organic production with local supply and assuming a local economic benefit to a broader understanding of the local “agro-food” economy and the connections between different types of farms and local and export markets.
3: Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H., Remington, T., … Torquebiau, E. F. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4(12), 1068–1072. https://doi.org/10.1038/nclimate2437
The paper discusses “Climate-smart agriculture” which is a strategy aimed at ensuring food security with regards to Climate change. Changes in rainfall and temperature patterns have been affecting agriculture. The livelihoods of those dependent on agriculture become more vulnerable, especially the poor. CSA aims to reduce these risks by increasing the adaptive capacity of farmers and improving resilience and resource use efficiency in agriculture. This would involve efforts from farmers, researchers, the private sector, civil society, and policy makers, with focus on; building evidence, increasing local institutional effectiveness, fostering policy coherence, and linking financing.
4: Ortiz, A. M., Outhwaite, C. L., Dalin, C., & Newbold, T. (2021). A review of the interactions between biodiversity, agriculture, climate change, and International Trade: Research and policy priorities. One Earth, 4(1), 88–101. https://doi.org/10.1016/j.oneear.2020.12.008
A section that I focused on discusses the impact of international trade on biodiversity and the environment. Nearly 1 billion people consume internationally traded food products, which leads to environmental impacts in the country of origin, mostly in developing countries. International trade drives 25% of bird species loss and more than half of the biodiversity impacts due to loss of suitable habitat from soybean production in the Brazilian Cerrado. Life cycle assessment (LCA) is emerging as a method for evaluating the end-to-end environmental impact of products and can link a final commodity to its associated biodiversity loss. However, it can be challenging to measure and aggregate impacts across a product's life cycle. The impact of international trade on biodiversity through climate change has not been considered, but countries could design trade policies that consider climate change and biodiversity to reduce damages. International trade also contributes to climate change through GHG emissions associated with traded commodities and their transport, which make up a small percentage of the total GHG emissions from food production. There is still a need for research to understand how international trade can be used to mitigate negative impacts or take advantage of benefits of climate change.
5: Lal, R. (2020). Home Gardening and urban agriculture for advancing food and nutritional security in response to the COVID-19 pandemic. Food Security, 12(4), 871–876. https://doi.org/10.1007/s12571-020-01058-3
The article states that the world cereal (?) production increased by 2.3% in 2019(?????), but the number of people facing severe food insecurity may double to 265 million by the end of 2020 due to the COVID-19 pandemic. The problem is severe in urban areas where the global population is projected to increase, causing food supply chain disruptions and increase in food waste. To address the issue, there is a need for more resilient food systems, reduced food waste, and strengthened local food production. Home gardening and urban agriculture can play an important role in advancing food and nutritional security and improving ecosystem services, but risks of soil contamination by heavy metals must be addressed.
6: Jansen, G., Cila, N., Kanis, M., & Slaats, Y. (2016). Attitudes towards vertical farming at home. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. https://doi.org/10.1145/2851581.2892474
This paper aimed to gain insights into people's attitudes towards small scale vertical farming technology for growing food at home. A prototype was developed that incorporated sensor and LED technology and was tested in a user study. The results of the study showed that people generally had a positive attitude towards the system, although a fully autonomous system was not preferred and concerns about food safety were raised.
7: Benke, K., & Tomkins, B. (2017). Future food-production systems: Vertical Farming and controlled-environment agriculture. Sustainability: Science, Practice and Policy, 13(1), 13–26. https://doi.org/10.1080/15487733.2017.1394054
This paper discusses the current challenges policy makers face with regards to providing food for the growing world population (expected to be 9.7billion by 2050). The issue of declining fertile land per person due to increasing population, urbanization and climate change was discussed. The paper highlights urban vertical farming as a potential solution because it involves the use of technology and automation for “land-use optimization.” Vertical farming aims to both increase productivity and reduce the environmental impact, whilst providing benifits (clean food source, biosecurity, protection from pests, droughts, and reduced transportation - using less fuel/less emissions). The article discusses pros and cons of vertical farming.
8. Banerjee, C., & Adenaeuer, L. (2014). Up, up and away! the economics of Vertical Farming. Journal of Agricultural Studies, 2(1), 40. https://doi.org/10.5296/jas.v2i1.4526
This article discusses the economic feasibility of Vertical farming. This was done by designing and simulating a 37 floor farm. The results showed that the farm could yield 3,500 tons of fruits and vegetables, and 140 tons of tilapia fillets, which is 516 times more productive than a traditional 0.25ha farm. The estimated costs were estimated to be 200million euros. The farm itself would require 80million litres of water and 3.5GWh of power power year. The cost of the produced food was estimated to be about 3.50euros to 4euros per kg. The article also states that more research is needed to optimize the production process to reap the full economic, environmental, and social benefits.
9. Al-Kodmany, K. (2018). The Vertical Farm: A review of developments and implications for the Vertical City. Buildings, 8(2), 24. https://doi.org/10.3390/buildings8020024
This paper discusses the growing need for vertical farming caused by food security concerns, urban population growth, limited farmland, and “food miles” (how far the food travels from farm to plate). The paper suggests that urban agriculture is a solution to these problems. Vertical farms combines food production and consumption in one place, and is stated to be suitable and an efficient option for urban areas where land is limited (and/or expensive). Advances in greenhouse technologies such as hydroponics, aeroponics, and aquaponics have made vertical farming more promising. Like the other papers mentioned prior to this one, this paper acknowledges obstacles with implementing vertical farms such as; economic feasibility, regulations, and lack of expertise.
10. Uphof, J. C. T. (1961). Dictionary of economic plants. The Quarterly Review of Biology, 36(4), 294–294. https://doi.org/10.1086/403521
This book/paper provides a comprehensive list of information on plants containing over 6000 economic plant species. Thee plants are listed alphabetically, includes scinitifc names, and descriptions. The book also includes groups of plants such as fungi, fiber sources, food plants, medicinal plants and food plants for livestock.
11. Hunt, R. (1990). Basic growth analysis. https://doi.org/10.1007/978-94-010-9117-6
This book is an introductory guide for students on the principles and practice of plant growth analysis. It provides a quantitive approach to describing and interpreting the performance of plant systems grown under various conditions. The methods described require simple experimental data and facilities.
12. Wöhrle, R. E., & Wöhrle, H. J. (2017). Basics designing with plants. Birkhäuser.
Helps understand how to design with plants where plants have specific needs and requirements. Although is more focused on designing WITH plants, can be useful to learn how they learnt about the limitations of plants.
Paul:
Morgan, B., Stocker, M. D., Valdes-Abellan, J., Kim, M. S., & Pachepsky, Y. (2020). Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study. Science of the Total Environment, 716, 135757. https://doi.org/10.1016/j.scitotenv.2019.135757
This article covers a way to determine the quality of the water used for irrigation on farms. By using a small Unmanned Aerial Vehicle (sUAV) with several ways of capturing images while above a body of water and a regression tree algorithm, the E. coli concentration of the water can be determined quite accurately.
Al-Rami, B. ., Alheeti, K. M. A. ., Aldosari, W. M., Alshahrani, S. M. ., & Al-Abrez, S. M. . (2022). A New Classification Method for Drone-Based Crops in Smart Farming. International Journal of Interactive Mobile Technologies (iJIM), 16(09), pp. 164–174. https://doi.org/10.3991/ijim.v16i09.30037
This paper covers a method for crop classification on farms using drone imagery. To do this it uses a Convolutional Neural Network (CNN), as well as using methods commonly found other image processing algorithms. They could reliably identify crops in a field with a variety of plants as well as diseases on plants, classified by small details like spots on leaves.
Sri, M.S., Nikita, V.M., Bhargavi, S.D., Muneeruddin, M., Pragthi, K., & Kranthi, T. (2021). APP BASED AUTOMATIC IRRIGATION SYSTEM.
The article describes a simple water management system for farms using WiFi. Using data from soil measurers underneath the ground were crops are, more water can either manually or automatically be irrigated to the specific areas, depending on the users preference. The automatic model compares the current moisture levels with standard expected values and either switches the water on or off withing acceptable ranges.
Olujimi, A., Aaron, I., Adebayo, O., Afolarin, A., Jonathan, E. (2022). Smart solar powered irrigation system. Journal Européen des Systèmes Automatisés, Vol. 55, No. 4, pp. 535-540. https://doi.org/10.18280/jesa.550413
This article considers an expanded and more sophisticated system for automatic irrigation compared to the previous article. Using a solar powered system which besides moisture levels also considers weather forecasts and soil temperatures, It can optimize water usage for crops on a farm, while not requiring any energy other than that generated by its solar panels. This means it is a valuable tool on farms without easy access to energy.
Mauri, P. V., Yousfi, S., Parra, L., Lloret, J., & Marin, J. M. (2022). The Usefulness of Drone Imagery and Remote Sensing Methods for Monitoring Turfgrass Irrigation. Advances in Intelligent Systems and Computing, 913–923. https://doi.org/10.1007/978-3-030-90633-7_78
This paper discusses various methods for drones to aid in monitoring crop health. Using IR sensors to acquire soil temperatures, RGB cameras for crop identification, and moisture sensors on the ground whose data can be logged by the drone, it was found that these low cost drones were very efficient and accurate in their readings. It is especially useful for grassland management, were large areas need to be monitored daily.
Lars:
1 C.W. Bac, E.J. van Henten, J. Hemminga , Y. Edan (2014); Harvesting robots for high-value crops: state-of-the-art review and challenges ahead, https://edepot.wur.nl/327202#page=19.
This article investigates the use of robots in the harvesting of crops. It has two main objectives: reviewing the state-of-the-art technology in automatic harvesting and formulating challenges for future research in the field. The performance of the harvesting robots are determined by various factors such as localization, speed, successful harvesting and probability of damage. It was found that the fruits were harvested successfully 66% of the time. The main challenge is dealing with the dynamic environment of a crop farm. Future research should be focused on this aspect.
2 Henten, Hemming, Tuijl, Kornet, Meuleman, Bontsema, van Os (2002); An Autonomous Robot for Harvesting Cucumbers in Greenhouses, https://link.springer.com/article/10.1023/A:1020568125418.
This article is about an automatic cucumber harvesting robot. The goal is to design a robot that is both fast and accurate. In order to achieve this goal the environment in which the robot operates is specified, greenhouses. The next step is to formulate concrete design specifications. For example, how the griper should like and vision system of the robot. Using these design requirement a prototype was built. It had an accuracy of 80% and needed 45s on average to harvest a cucumber.
3 Henten, Bontsema (2009); Time-scale decomposition of an optimal control problem in greenhouse climate management, https://www.sciencedirect.com/science/article/abs/pii/S0967066108001019.
A model to regulate the climate in greenhouses is proposed in this article. It should maximize the effectiveness of greenhouses. To do this, the optimal trade-off between food production and energy consumption needs to be found. A hierarchical system is used in the model. This means that the components of the systems are linked to each other in a hierarchical manner from top to bottom. The proposed model should be able to deal with fast changing weather conditions and the slow food growing process simultaneously.
4 V. Dharmaraj* and C. Vijayanand (2018); Artificial Intelligence (AI) in Agriculture.
This paper analyses the role of AI in farming. It sees AI technologies as a way to improve food production and ensure that the growing world population can be fed. There are various technologies that are useful in an agricultural context such as disease detection, checking the ripeness of crops, field management and automatic irrigation. This should make farming both more profitable and sustainable in foreseeable future.
5 Selvaraj, Vergara, Ruiz, Safari, Elayabalan, Ocimati, Blomme (2019); AI-powered banana diseases and pest detection, file:///C:/Users/20213415/Downloads/s13007-019-0475-z.pdf.
An AI models for detecting diseases in banana crops are designed and tested. The AI use (dcnn) deep convolutional neural networks and are trained with transfer learning. Transfer learning is a method to train AI in which the program is shown many pictures of a certain phenomenon, in this case crop diseases. Afterwards it should be able to detect new pictures of the same theme. The best AI were able to detect diseases in banana crop more than 90% of the time.