THE PROBLEM AND ITS SETTING
The researchers chose to make an Automated Agricultural Apparatus (AAA). Farmers always spend countless years under the blazing sun being bathe by its scorching heat. Farmers usually come home with their backs aching due to being bent all day. The group was able to find a way to make farming easier by using an apparatus as the main component of agriculture. With this farmers can go home early and use their spare time to rest and bond with their family. The researcher’s main consumer target would be in Doha, Qatar. Qatar’s role in the agriculture market is not noticeable due to some factors; the oil market is more prioritized, location for growing crops with good soil is challenging, and high demand only on imported organic products than locally produced goods. With these reasons, the group concluded that by using this machine, productivity rate may increase, demand for local products is high, competition in the agricultural market is challenged and better efficiency throughout. The group has decided that this machine would be affordable to any agriculture related businesses and also to the public as well. The MSRP (Manufacturer’s suggested retail price) of the Automated Agriculture Apparatus (AAA) would be ranging to QAR. 50 -100. With the price range, materials producing the machine would be fairly durable yet affordable to anyone.
BACKGROUND OF THE STUDY
Automated Agricultural Apparatus (AAA) is a machine that was produced by the researchers with the purpose of cultivating, managing, and maintaining plants in order to aid farmers or anyone who likes plants. The Automated Agricultural Apparatus (AAA) is capable of planting and watering plants through the dispenser that moves back and forth above the Automated Agricultural Apparatus(AAA). Though the Automated Agricultural Apparatus (AAA) has certain limitations due to its demands of being near a faucet and a power socket at the same time, the Automated Agricultural Apparatus (AAA) will still work even if it is left for a period of time. The Automated Agricultural Apparatus (AAA) can plant potatoes, bean sprout, tomato, and other small plants that does not require much space. The Automated Agricultural Apparatus (AAA) can be made with the use of items that can be found anywhere. The researchers designed the Automated Agricultural Apparatus (AAA) in a way that the only thing that the person-in-charge will do is to check whether the dispenser has enough amount of seeds, if the plant is ready for harvest and lastly to plow the soil that was previously planted on. The Automated Agricultural Apparatus (AAA)
STATEMENT OF THE PROBLEM This study aimed to find out the possibility of making the Automated Agricultural Apparatus (AAA). Specifically, this study sought to answer the following questions;
1. How long will it take to build the Automated Agricultural Apparatus (AAA)?
2. How does it affect the environment?
3. Is the product effective as an agricultural tool in terms of the following:
c. Operations Capacity
It is not possible to build the Automated Agricultural Apparatus (AAA).
SIGNIFICANCE OF THE STUDY
The product that the researchers are planning to develop can help the community by economically saving money and creating an alternative to buying expensive pre-built machines. This study is not only beneficial financially but it is also very easy to do and the materials are easily accessible. The researchers’ product can also be a benefit to the environment by lessening the waste thrown away. As students, the benefits of this project are to help lessen the burden of the students who undergo financial problems on how they budget their money. This project will also help enhance the resourcefulness of the researchers by using materials that are not being used or are just thrown away. Not only can it help the students but also the community.
A) PSD Student:
This study benefits the students by teaching and instructing them on how to build the Automated Agricultural Apparatus (AAA) with some materials commonly found in the school or at home, it can also boost their resourcefulness and creativity by looking for other alternatives.
B) PSD Faculty:
Teachers can save money by building the Automated Agricultural Apparatus (AAA) and at the same time can teach the students on how to build this machine at home and help clean the environment.
C) Filipino Community:
People who have who have financial problems can rely on building the Automated Agricultural Apparatus (AAA) and save money doing it since the materials used are inexpensive. They can also help make the environment cleaner by lessening the wastes thrown.
SCOPE AND LIMITATIONS
Group 2 of 11-Titanium, conducted a study about the Automated Agricultural Apparatus (AAA). The factors that should be considered is the Qatar’s role in the agriculture market is not noticeable; the oil market is more prioritized, location for growing crops with good soil is challenging, and high demand only on imported organic products than locally produced goods. The researchers will conduct tests regarding the study of the Automated Agricultural Apparatus (AAA) which was to be conducted in Philippine School Doha. The group has estimated that the process of building the machine can just be finished in 3-5 days however the study and experimentations are estimated to be done in a month. The finished product size of the finished product will be around 77 cm in length and 50 cm in width. The product requires a water and power supply nearby, the ideal places would be the backyard, terrace and the rooftop
REVIEW OF RELATED LITERATURE
This chapter provides the related literature to help understand and comprehend the study that is found in three classifications.
A) Related Studies
According to Applied machine vision of plants: a review with implications for field deployment in automated farming operations by C. L. McCarthyl et al (2010), Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.
As stated in Implementing Precision Agriculture in the 21st Century by John V. Stafford (2000), precision agriculture has generated a very high profile in the agricultural industry over the last decade of the second millennium—but the fact of ‘within-field spatial variability’, has been known for centuries. With the advent of the satellite-based Global Positioning System, farmers gained the potential to take account of spatial variability. The topic has been ‘technology-driven’ and so many of the engineering developments are in place, with understanding of the biological processes on a localized scale lagging behind. Nonetheless, further technology development is required, particularly in the area of sensing and mapping systems to provide spatially related data on crop, soil and environmental factors. Precision agriculture is ‘information-intense’ and could not be realized without the enormous advances in networking and computer processing power.
Based on Aspects of Precision Agriculture by Francis and Nowak (1999), Precision agriculture is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality. Success in precision agriculture is related to how well it can be applied to assess, manage, and evaluate the space-time continuum in crop production. This theme is used here to assess the current and potential capabilities of precision agriculture. Precision agriculture is technology enabled. It is through the integration of specific technologies that the potential is created to assess and manage variability at levels of detail never before obtainable and, when done correctly, at levels of quality never before achieved. The agronomic feasibility of precision agriculture has been intuitive, depending largely on the application of traditional management recommendations at finer scales, although new approaches are appearing. The agronomic success of precision agriculture has been limited and inconsistent although quite convincing in some cases, such as N management in sugar beet (Beta vulgaris L.). Our analysis suggests prospects for current precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, management parameters with high spatial dependence and low temporal variance (e.g., liming, P, and K) will be more easily managed precisely than those with large temporal variance (e.g., mobile insects). The potential for economic, environmental, and social benefits of precision agriculture is complex and largely unrealized because the space-time continuum of crop production has not been adequately addressed.
According to Agricultural Automation: Fundamentals and Practices by Qin and Pierce (2013), agricultural automation is the core technology for computer-aided agricultural production management and implementation. An integration of equipment, infotronics, and precision farming technologies, it creates viable solutions for challenges facing the food, fiber, feed, and fuel needs of the human race now and into the future. Agricultural mechanization, one of the top ranked engineering accomplishments in the past century, has created revolutionary change in crop production technology and made it possible to harvest sufficient products to meet the population’s continuously growing needs. Continued progress is essential to the future of agriculture. This book provides an up-to-date overview of the current state of automated agriculture and important insight into its upcoming challenges.
According to JIAC2009 – Book of abstracts by Lokhorst, C., et al (2009), ICT in agriculture, the field of EFITA\’s interest, precision agriculture and precision livestock farming are becoming ever more relevant as the agricultural industry struggles to come to terms with various developments. These include issues of cooperation, Internet, standardization, software architecture, robotics, environment, animal and human welfare, economics, traceability, farm management, vehicle guidance, crop management, animal disease and livestock management. Whilst some benefits have proved elusive, others contribute positively to today\’s agriculture. Research continues to be necessary and needs to be reported and disseminated to a wide audience.
According to Geospatial and Information Technologies in Crop Management by Steven T. Sonka, et al (1997), sensors, satellite photography, and multispectral imaging are associated with futuristic space and communications science. Increasingly, however, they are considered part of the future of agriculture. The use of advanced technologies for crop production is known as precision agriculture, and its rapid emergence means the potential for revolutionary change throughout the agricultural sector.
C) Newspapers and Magazines
In manilastandard.net, Bloomberg (2016) Manila Standard. Jin Kawaguchiya gave up a career in finance to help revive Japan’s ailing dairy industry―one robot at a time.
In a country that relies increasingly on imported foods like cheese and butter, Japan’s milk output tumbled over two decades, touching a 30-year low in 2014. Costs rose faster than prices as the economy stagnated, eroding profit, and aging farmers quit the business because they couldn’t find enough young people willing to take on the hard labor of tending to cows every day.
But technology is altering that dynamic. On the northern island of Hokkaido, Japan’s top dairy-producing region, Kawaguchiya transformed the 20-cow farm he inherited from his father-in-law 16 years ago into Asia’s largest automated milking factory. Robots extract the white fluid from 360 cows three times a day and make sure the animals are fed and healthy. The machines even gather up poop and deposit it in a furnace that *generates electricity.**
“Without robots, I would have to hire as many as 15 part-time workers to take care of cows,” Kawaguchiya, 44, said during an interview at the dairy in Kakuyama. “I can save 15 million yen ($146,000) a year thanks to them.”
In sfgate.com, New York Times (2016), the new era centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial Internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.
The shift was evident in a Lowe’s home improvement store this month, when a prototype inventory checker developed by Bossa Nova Robotics silently glided through the aisles using computer vision to automatically perform a task humans have done manually for centuries.
According to agweb.com, Potter (2016), Driverless tractors and other autonomous farm equipment probably sounds like they belong in a futuristic science-fiction novel – not as a serious technology conversation in 2015. But get ready, it’s probably going to be a reality sooner than you think.
That’s because earlier today, parent company Daimler automotive corporation announced its Freightliner “Inspiration Truck” will be the first autonomous commercial truck to drive on American roads. According to Car and Driver blog, it comes equipped with a front radar sensor, stereoscopic cameras and other sensors that help the truck situate itself on the road. The truck can read road signs and traffic signals on its own – not to mention drive in a way that optimizes fuel economy.
Drivers are still present in these vehicles – they have the option to drive the vehicle or engage the autonomous mode.
“What if the driver could do whatever he wants?” Freightliner asks in this proof-of-concept video. “His duties would be redefined. He would become a logistics manager while driving.”
According to Quartz, Coren (2016), more than a million people working in America’s fields, and far more globally, are about to face competition from workers who never sleep, get tired or ask for a living wage. As field robots have gotten cheaper, a steady stream of farm jobs are being automated. Lettuce weeding is one of the first where the cost of robots now matches human labor. “Agriculture for hundreds of years has been an intuition business,” says Lux’s lead agricultural analyst Sara Olson in an interview. That will end as “precision agriculture” brings data and automation to traditional tasks, making farming more productive and profitable, she predicts. At first, robots will make existing jobs more productive. But jobs will ultimately be lost as robots assume more and more of the work. “Over time, there would have to be a shift,” says Olson. “It will happen slowly enough that I see an opportunity for people who want to be in the industry to learn how to operate machinery, manufacture the equipment, and service and support these new systems.”
Robots will likely make inroads fastest in areas where the labor is backbreaking, and peak harvest times create a short supply of workers. Most robots have been built for specialized tasks: grapevine pruners, lettuce thinners, strawberry picking and cow-milking robots. But corn and other commodity crops are already taking advantage of economies of scale to get ahead of the cost curve. Large corn farmers in the US are buying features like self-steering tractors to save money. Even though the technology isn’t expected to reach price parity with human labor until 2020 for most farmers, about 10% of US farmer have adopted the technology because of their scale, reports Lux.
In IDTechEx, Ghaffarzadeh (2016), robotic and drones have already started to quietly transform many aspects of agriculture. Already, thousands of robotic milking parlours have been installed worldwide, creating a $1.9bn industry that is projected to grow to $8bn by 2023. Tractor guidance and auto steer technologies are also going mainstream thanks to improvements and cost reductions in RTK GPS technology. Indeed, more than 300k tractors equipped with auto steer or tractor guidance will be sold in 2016, rising to more than 660k units per year by 2026. Unmanned autonomous tractors have also been technologically demonstrated with large-scale market introduction largely delayed not by technical issues but by regulation, high sensor costs and the lack of farmers\’ trust. This will all change by 2022 when sales of unmanned or master-slave (e.g., follow me) tractors picks up.
In a Agribotics Podcast Big Ag, Agricultural Robotics, Factory Farms and Orchards, Green (2016), the world’s population is expected to hit more than 9 billion by 2050. That’s a lot of mouths to feed. To grow all that food, the world’s farms will need to increase production by around 25 percent, according to a recent report from the World Resources Institute. To make matters worse, experts expect shortages of water, fertilizer, and arable land to make it even more difficult to feed future generations. At the same time, the number of people involved in the often dangerous world of agricultural labor is decreasing — an effect that is especially rapid in Europe and the United States. The solution to automate agriculture as quickly as possible and as extensively as possible holds out the possibility that technology can help avert worldwide shortages of food in the coming decades. Tom Green checks in with Frank Tobe, publisher of The Robot Report and founder of Robo-Global ETF, to assess the agricultural robotics landscape, future of automated farming, factory farms, agricultural drones, investing opportunities in automated farms and the way forward to increasing harvests enough to better feed the planet’s 7.5 billion people.
As said in a Robotics Business review article Agricultural Robots Help Australian Farms Boost Productivity, Edwards (2016), approximately two-thirds of Australia’s land is dedicated to farming. Although cattle grazing remains that country’s highest-value farm production sector, the next highest-value products are wheat, dairy, vegetables, and fruit and nuts, before lamb meat and wool, according to an Australian government report. Australian agribusiness faces several key challenges, including access to fresh water, overgrazing, transportation costs, and feral animals. To deal with these issues, Australian farming has grown increasingly inventive and reliant on technology. Robotics now promises to help Australian agriculture become more productive in an increasingly competitive global market.
In the website IEEE Robotics & Automation Society, Takodoro (2015), agriculture is humankind’s oldest and still its most important economic activity, providing the food, feed, fiber, and fuel necessary for our survival. With the global population expected to reach 9 billion by 2050, agricultural production must double if it is to meet the increasing demands for food and bioenergy. Given limited land, water and labor resources, it is estimated that the efficiency of agricultural productivity must increase by 25% to meet that goal, while limiting the growing pressure that agriculture puts on the environment.
Robotics and automation can play a significant role in society meeting 2050 agricultural production needs. For six decades robots have played a fundamental role in increasing the efficiency and reducing the cost of industrial production and products. In the past twenty years, a similar trend has started to take place in agriculture, with GPS- and vision-based self-guided tractors and harvesters already being available commercially. More recently, farmers have started to experiment with autonomous systems that automate or augment operations such as pruning, thinning, and harvesting, as well as mowing, spraying, and weed removal. In the fruit tree industry, for example, workers riding robotic platforms have shown to be twice as efficient as workers using ladders. Advances in sensors and control systems allow for optimal resource and integrated pest and disease management. This is just the beginning of what will be a revolution in the way that food is grown, tended, and harvested.
In Technocracy News & Trends, Dobbs (2015), with automated farming, workers and traditional family farmers will be quickly and easily excessed. The race will be on to completely subsume small farms into giant corporate farms that exercise monopoly control over food production, and advanced technology will drive the whole process. Those who have the technology will thrive; those who don’t will wither away. For centuries, farming was an intuitive process. Today, it’s networked, analytical, and data-driven. Large farms (1,000 acres or more) started the trend, adopting the tools of precision agriculture—using GPS-guided tractors, drones, and computer modeling to customize how each inch of land is farmed. Farm managers can measure and map things like soil acidity and nitrogen levels, and then apply fertilizer to specific plants—not just spray and pray. As a result, they get the most out of every seed they plant. Such methods have reduced farm costs by an average of 15 percent and increased yields by 13 percent, according to a 2014 survey by the American Farm Bureau Federation. Small farms—which make up 88 percent of all farms in the U.S., according the Department of Agriculture—are now adopting similar methods, powered by a proliferation of affordable sensors, drones, cameras, wireless networks, and data plans. And they sometimes see better results than large farms. Cox, for one, says he has cut labor and fertilizer costs by as much as 70 percent, and in some cases doubled his crop yields.
“In the future, the machines will be out there doing work and at the same time learning about that field,” says Cavender-Bares.
According to Robohub.org, Sparc (2014), robots are just part of an overall push towards precision agriculture. Given the potential, Europe has funded at least 6 projects around robotic farming. And there is plenty to do given the large number of tasks on a farm that are ripe for automation.
For crop farming, robots need to autonomously navigate their environment and perform actions at set locations, for example, picking a fruit, spraying a pesticide, planting a seed, imaging a plant, or making a measurement. Glasshouses are slightly simpler to move around since the environment is more carefully engineered, and is often fit with tracks which robots follow to reach desired locations. In the case of outdoor farming, the robots work by receiving a plan with a set of locations to visit on the field. When the robot trajectories are known, the robot can use GPS positioning and a closed-loop control to make sure it remains on track. When the task is to follow an unknown trajectory, for example a crop row, vision is often used to allow the robot to find its way. Robots are wirelessly connected to a central operator to both receive updated instructions regarding the mission, and report status and data. Put together, making an autonomous farm robot requires clever controllers, localization and communication systems. To a certain extent, the technology is similar to that of autonomous cars applied to agtech. Where it differs is that farming robots often need to manipulate their environment, picking vegetables or fruits, applying pesticides in a localized manner, or planting seeds. All these tasks require sensing, manipulation, and processing of their own.
The study made use of motors, gears, sprinkler, wires, chains, seeds and water. Although wood can be seen in the product it can be replaced with other sturdy materials, therefore it was not considered as a main component. First, the researchers constructed the frame with the use of wood. Then the researchers made the dispenser, which contains all of the main components of the study. This process was also the most difficult the product will not run without the dispenser. Lastly, the researchers combined the dispenser and the frame.
DEFINITIONS OF TERMS
Agriculture -The study is useful to agriculture as it aims to help farmers with their jobs
Apparatus- A machine/equipment used for a certain activity.
Automated- The goal of the researchers was to make an apparatus that will help the farmers by automatically planting seeds and watering them.
Chains- Used to guide the movement of the dispenser.
Drill- Used to make the necessary holes needed for the product.
Functionality- It is the measure of how long can an object work before the need of replacement.
Gears- Served as a wheel in the product.
Jigsaw- Used to cut the wood that was used create the frame and other woodworks.
Motor- It made the dispenser move back and forth.
Operational Capacity- It is the measure of up to what the product can hold or how long can it run without the need of a supervisor.
Sensors- Is an object whose purpose is to detect events or changes in its environment, and then provide a corresponding output.
Soldering Iron- Used to fix the wirings of the product.
Usability- The measure of how useful the product is.
The study is an experimental design. This is because the researchers gathered information all throughout the study. The researchers differentiated each test that will be made. The design answered the researchers’ hypothesis. The study is an experimental design because the researchers are concerned with the analysis of data.
DATA GATHERING PROCEDURE
The researchers gathered data through a series of tests and prototypes. The researchers first did some concept designs with the use of a program called ‘AutoCAD’. Then the researchers made a model using foam boards to use if the concept was plausible. After that the researchers with the data they have gathered with the 1st model made some changes for the 2nd model. The process of making a sample model and gathering data was repeated numerous times. Through the data gathered the researchers were able to make some changes to further improve the product.
1. Box Electric fan motor
2. Plywood, Wood
6. Metal gears
7. Skateboard wheels and Bearings
8. Toggle switch
9. Plastic bottles
3. Tape measure
4. Glue gun
8. Soldering iron and lead
10. Extension Wires
11. Duct Tapes
12. Electrical Tape
13. Zip Tie
1. Gather everything that you need.
2. Construct the rectangle base box as shown below:
3. Construct the railing for the dispenser.
4. Proceed in creating the dispenser and at the same time arrange the AC motor and the electrical wirings.
5. Place the water sprinkler under the railing and connect the hose to it.
6. Align the dispenser’s gear to the railing.
7. Turn on the motor and place the seeds in the container.
8. Add the soil.
PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA
In the fourth figure, the researchers made use of foam boards to create the estimated output that the researcher imagined with the use of AutoCAD. The prototype gave the researchers the ideal base that they wanted.
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