The objective of this proposal is to inform the company about the benefits of using agricultural drones, the total budget, the capabilities of the drones, and the required equipment. The proposal also includes a projection of the expected outcome and strategy for evaluating the results. In addition, the advantages of using agricultural drones are discussed, as well as their feasibility.
Essentially, investing in agricultural drones is the way forward for a growing company, such as Manayer Farms. The project will, therefore, be profitable to the enterprise. I look forward to a positive response from you.
Table of Contents
List of Illustrations 4
Materials and Equipment 9
Expected Results 9
Plan for Evaluating Results 10
List of Illustrations
Figure 1 14
Figure 2 14
Remote Sensing: Scanning the earth using a drone or satellite to obtain information.
Return on investment (ROI): The ratio of the net profit to the cost of investments, which is then multiplied by one hundred.
Algorithm: A set of instructions used in calculations and operations by computers and software.
SaaS (Software as a Service): A cloud computing model, whereby a third-party hosts applications that clients can access.
Normalized difference vegetation index (NDVI). A digital indicator that uses visible and near-infrared light to analyze the health of plants based on the difference in the light that they reflect.
Herein is a proposal regarding the use of agricultural drones in farming and how Manayer Farms can benefit from the technology. The company is headquartered at Riyadh, Saudi Arabia whose hot desert climate demand for smart farming practices. The research covers how the drones are operated, their capabilities, and the equipment required. Additionally, the expected results are projected using available data and literature from studies and financial information. The plan that will be used to evaluate the results is straightforward, and it will involve comparing gathered information in a span of two years. Moreover, the drones are quite easy to operate as they are created to be user-friendly. The budget for the initial investment is expected to be relatively high, but the ROI projected will increase the profit margin of the company. Overall, the use of agricultural drones is justifiable due to their several benefits, such as reduced production cost and high-quality yields.
The employment of remote sensing technology in agriculture has revolutionized agricultural practices with the aim of reducing operation costs and increasing the profit margin. In particular, the use of drones in farming is increasingly becoming popular due to the high ROI, reduced effort, and high efficiency (Li, 2010). Some of the benefits of using UAVs in agriculture are increased yields, cost-effective, time-effective, monitoring crop progress, smart irrigation, and water efficiency. Water conservation is vital because the climate of Riyadh is classified as a hot desert. Drones are reliable since they execute control algorithms, which are precise and help in data processing and management (Li & Chen, 2013). For that reason, this report is aimed at evaluating and assessing the viability of integrating agricultural drones into the farming activities of Manayer Farms and how it can reduce cost and increase productivity.
Agricultural drones can be programmed to collect accurate data that can help both the engineers and farmers at Manayer to monitor operations and crop health without being present at the field. Moreover, Riyadh has a hot desert climate, which makes it challenging for the farmers to manually survey the farms. In addition, the data collected uses the software as a service (SaaS), whereby data is collected and uploaded to the cloud for rapid processing and users receive immediate feedback (Zhang & Pierce, 2013). The primary function of the engineers and farmers is to act on the information provided from the analysis. Additionally, the personnel will require training for operating and programming the agricultural drones. Furthermore, drones are nowadays user-friendly and less time and capital are needed for training.
Agricultural drones contain a fail-safe, which enables them to return to the initial point of take-off (Cai, Chen & Lee, 2011). Therefore, minimal time is required to monitor the UAVs and the farms since the entire process can be automated. In addition, drones can be programmed for crop surveillance at any interval. However, since Riyadh has a hot desert climate, the drones will be programmed to survey the farms on an hourly basis to closely monitor crop health and progress, as well as ensure all areas are irrigated. With the fail-safe option and the automation capability, they significantly reduce the time used for crop and animal surveillance in farms (Zhang & Kovacs, 2012). Both the engineers and the farmers should be trained for two to four weeks, and after the drones have been commissioned, the staff will need a training program that updates their knowledge and skills concerning operation and programming of agricultural drones.
Drones have a broad range of capabilities that are beneficial to farming. Drones can be used for soil and field analysis, planting, crop spraying, crop monitoring, irrigation, and health assessment (McBratney, Whelan, Ancev & Bouma, 2005). The UAVs save time, reduce workload, increases efficiency with the precision technology, and are easy to operate. The drones are used for crop health imaging, whereby the sensors collect information based on the light reflected by plants (Gao & Shaw, 2004). Figure 1 is an example of how RGB color differs from NDVI (Normalized Difference Vegetation Index), which reveals variations in color depending on the crop health. Moreover, the NDVI images enable engineers and farmers to identify dead, stressed, and healthy leaves that have different colorations as illustrated in figure 2. Moreover, farmers can use the NDVI images to identify areas that do not have sufficient supply of water, which could also help in revealing clogged nozzles (Dagar et al., 2016). In fact, the drones can also be programmed to monitor a particular area by fencing, which will act like a borderline.
Materials and Equipment
Besides purchasing the drones, the company will need to obtain a license to be able to fly them. Drones can fly around 50 to 100 meters high, but special permission is required to fly above 50 meters (Austin, 2011). In addition, SaaS for processing data and a flight controller. Also, the farms should be fenced to draw a map that will guide the UAVs to avoid crossing restricted territories.
On average, resorting to visual inspections and aerial survey to take images of crop fields cost about $2. However, the return on investment (ROI) on the purchase of agricultural drones can be met quickly, and the ROI can be achieved in one crop season. Thus, by having ownership of the UAVs, the farm is likely to reduce the cost of operation and also improve crop yield (Zhang, 2015). Furthermore, the firm can save more capital and reduce food wastage. The ROI is approximated to increase by ten percent in the first two seasons after implementing smart farming using agricultural drones.
Plan for Evaluating Results
Since agricultural drones can monitor the progress of crops in the farm, the engineers and farmers at Manayer Farms will have sufficient and reliable information to make effective decisions. The data collected will be compared in a span of two years to ascertain the reliability of agricultural drones for smart farming, which is the future of agriculture. Moreover, the financials will reveal that the technology is a sound investment.
The agricultural drones have a GPS functionality and can automatically return to the point where they took off (Stafford & Werner, 2003); therefore, they are easy to control. In addition, the UAVs have the advantage of big data analytics, a characteristic that traditional farming does not possess. The data collected is automatically analyzed and processed, and the resulting information is relayed to the user who is not required to have analytical or professional skills.
The initial investment to integrate agricultural drones into farming are always significant, but the ROI projected justifies the investment. Moreover, agricultural drones are advanced tools that gather complex data. The purchasing price for a complete drone system ranges from $1,700 to over $30, 000. After comparing prices and capabilities, the company will spend $14,000 on one drone. A total of two drones, which is the number required, will cost $28, 000. The company will spend an additional $400 on training its farmers and engineers. Other expenses such as obtaining a license and buying accessories will amount to approximately $2,800. The total cost of the project is $31, 200.
Drones can reveal patterns that can help identify irrigation problems, soil variation, pest and fungal infestations, and progress of crop health, all these variables are not apparent at an eye level (Gao & Shaw, 2004). The survey can be programmed to create a time series that shows changes in the crops, even the slightest ones that are typically overseen. Smart farming is healthy for ROI and crop yield.
Agricultural drones are embraced in the western world since their efficiency and a high ROI are satisfactory. They are also easy to operate considering that the data collected is automatically analyzed. In addition, setting up the UAVs and obtaining a license are affordable, and they also have a broad range of capabilities that increases efficiency in agriculture. Technology has been gaining recognition in the agricultural sector in the last four years, and that trend is expected to progress steadily in the next decade (Sonka & Cheng, 2015). Therefore, a growing company like Manayer Farms in Riyadh, Saudi Arabia stand to benefit a lot from the agricultural drones, and considering that the climate is a hot desert, water efficient irrigation can be put into practice. Overall, agricultural drones can reduce the operating costs and improve the quality of crop yield, as well as save time. For that reason, agricultural drones are a sound investment for a company, such as Manayer Farms.
Austin, R. (2011). Unmanned aircraft systems: UAVs design, development and deployment (Vol. 54). New York, NY: John Wiley & Sons.
Cai, G., Chen, B. M., & Lee, T. H. (2011). Unmanned rotorcraft systems. Berlin, BER: Springer Science & Business Media.
Dagar, J. C., Sharma, P. C., Sharma, D. K., & Singh, A. K. (2016). Innovative Saline Agriculture. New York, NY: Springer.
Strojnik, M., Shaw, D. R., Gao, W., & Wei, G. (2003). Ecosytems’ dynamics, agricultural remote sensing and modeling, and site-specific agriculture: 7 august 2003, San Diego, California, USA; [part of SPIE’s international symposium on optical science and Technology]. Bellingham, WA: Society of Photo Optical.
Li, D. (2010). Computer and Computing Technologies in Agriculture III: Third IFIP TC 12 International Conference, CCTA 2009, Beijing, China, October 14-17, 2009, Revised Selected Papers (Vol. 317). Berlin, BER: Springer Science & Business Media.
Li, D., & Chen, Y. (Eds.). (2013). Computer and Computing Technologies in Agriculture VI: 6th IFIP TC WG 5.14 International Conference, CCTA 2012, Zhangjiajie, China, October 19-21, 2012, Revised Selected Papers (Vol. 393). New York, NY: Springer.
McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). “Future directions of precision agriculture.” Precision agriculture, 6(1), pp. 7-23.
Sonka, S., & Cheng, Y. T. (2015). Precision agriculture: Not the same as big data. Farmdoc Daily.
Stafford, J. V., & Werner, A. (2003). Precision agriculture. Wageningen: Wageningen Academic Publishers.
Zhang, C., & Kovacs, J. M. (2012). “The application of small unmanned aerial systems for precision agriculture: A review.” Precision agriculture, 13(6), pp. 693-712.
Zhang, Q. (Ed.). (2015). Precision Agriculture Technology for Crop Farming. Florida, FL: CRC Press.
Zhang, Q., & Pierce, F. J. (Eds.). (2013). Agricultural automation: Fundamentals and practices. Florida, FL: CRC Press.
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