Problem Identification

Water shortages, and their implications are a problem experienced around the globe, including the United States. However, in underdeveloped countries where water infrastructure is especially lacking, efficient use becomes even more essential. Agricultural production is a major consumer of this limited resource, and many irrigation practices are inefficient in their application of water. An embedded system could directly address this problem in two ways: by monitoring soil moisture and watering only when necessary, as well as monitoring ambient weather conditions and watering only at ideal times for water absorption. Based on previous work relating to this idea, not only would this system reduce the amount of water used, but could also increase crop production by ensuring consistent water for growth.

Background

While there are commercial options currently available, they are large in scale, would foreseeably require complex maintenance (due to the large number and variety of parts) and are currently cost prohibitive [1]. This degrades the feasibility of such systems in remote, or underdeveloped areas of the globe. Instead, we looked into implementations in a microcontroller context, where cost could be hopefully be kept down. Additionally, weather research was discussed, to help in our development of an algorithm to successfully use ambient weather data.

Existing Solutions

i. Automatic Irrigation

Researchers at Annamalai University implemented an automated irrigation system using an embedded microcontroller[2]. In this implementation, the goal was idealized watering for a specific plant. Due to the complexities of growing cardamom, the embedded controller monitored temperature, humidity and moisture of the soil. These variables were monitored remotely throughout the field, then used to actuate individual solenoid valves to water specific areas. The objective of this process was to maximize growing, however, they did conclude that water efficiency increased through this process.

A second approach was used by Kumbhar and Ghatule, with the intention of maximizing production through automated irrigation using a microcontroller[3] Breaking each field into four sectors, they monitored the moisture content in the soil, watering each respective area until the moisture readings hit objective levels.

The University of Minnesota Extension office also explored moisture sensor usage in determining optimal irrigation methods[4]. In their method, they described a way to “learn” the water holding capacity of the soil, by measuring the applied amount of water, and the response of the soil 24 hours after application. Based upon the relative increase or decrease of soil moisture, differing amounts of water could be applied. Their suggested solution was remote sensors, which could be monitored by an individual in charge of the irrigated field or area. Additionally, the sensor options were all listed at or above $50USD.

ii. Effects of Temperature and Humidity on Plant Growth

Two of the main interactions that occur with water surrounding plant growth is evaporation and transpiration [5]. Evaporation is water lost when converted to water vapor (in this context, from the ground), while transpiration is the evaporation of liquid water contained in plant tissues and its subsequent release into the atmosphere. These interactions have a large impact on irrigation, as they change throughout the growing cycle. Immediately after planting, nearly 100% of water is lost through evaporation, while at full crop transpiration accounts of nearly 90% of water loss.

Researchers at the National Laboratory for Agriculture and the Environment investigated the effect temperature extremes had on plant growth and development [6]. Unsurprisingly, they found that deficit water reduced biomass. However, it was their experiments with excess water that were the most shocking. They found that over watering at normal temperatures had a noticeable impact on plant development, but over watering at extreme temperatures was most damaging. This was surprising because the excess water was thought to aid in the self-cooling mechanisms of the plants.

A group of Georgian researchers explored optimal irrigation rates for agricultural crops when considering weather along with soil conditions [7]. In their study, they calculated the water necessary based upon the specific crops’ evapotranspiration constant as it changed throughout the growth cycle. By using different irrigation configurations, they were able to determine that drip irrigation in conjunction with mulching and covering techniques drastically reduced the water necessary for optimum plant growth. In fact, water use efficiency was 2.5 higher than traditional surface furrow irrigation, while yield was increased by 72%.

System Organization

i. Soil Moisture Sensing

The cornerstone of our system is the ability to read and act on soil moisture information. When the measured moisture drops below a user-configurable threshold, the system enters into the irrigation sequence, making subsequent determinations based on other peripherals. Additionally, the water sensor is used in our water control algorithm to allow for error determination.

ii. Optimal Temperature Locating

While the term optimal may be considered in several contexts, within the scope of this project we mean it to be the most efficient use of water. Therefore, due to the evapotranspiration that was discussed in the research, application of water during cooler parts of the day is desirable for maximum absorption into the soil and root system of the crop. We make this determination by monitoring the most recent six hours worth of temperature data (sampling on the hour), comparing readings and checking for three decreases in temperature. This algorithm was chosen by looking at temperature data from a variety of geographical locations within the United States (due to the availability of data). Below are three examples, the first from Easton, PA due to the testing that would occur, with the second two chosen due to the prevalence of agriculture in the respective area. Our algorithm would recognize an optimal watering time within the marked areas of the trending temperature data. This decision is then used to advance through the irrigation sequence.

iii. Water Control

Water control is achieved through the use of a solenoid valve for flow control, and a flow sensor to monitor the amount dispensed. Once the user-configured amount of water is dispensed, the valve is shut and a wait period is initiated to allow the ground time to saturate. After this wait time, the moisture error is calculated, and normalized. This normalized error is then added to, or subtracted by, 1 and multiplied by the target water amount, adjusting the value so subsequent sessions will achieve desired moisture levels.

iv. User Interface

An LCD screen is used to communicate the system’s performance and actions to the user, as well as allowing for manual watering when it is desired. Relevant information displayed is:

• Time and Date
• Current Temperature
• Current and Target Moisture
• System state
• Current and Target Amount of Water used
• Last Watering time
• Valve Closed/Valve Open/Add Water Alert
• Manual Water Button

 

[1] FarmSolutions has redefined affordability with a low starting price of $499. (n.d.). Retrieved October 23, 2016. https://farmsolutions.com/remarkably-different/roi/

[2] Ramya, V., Palaniappan, B., & George, B. (2012, September). Embedded System for Automatic Irrigation of Cardamom Field using Xbee-PRO Technology. International Journal of Computer Applications, 53(14). Retrieved October 23, 2016, from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.258.9595&rep=rep1&type=pdf

[3] Kumbhar, S. R., & Ghatule, A. P. (2013, March 13). Microcontroller based Controlled Irrigation System for Plantation. Proceedings of the International MultiConference of Engineers and Computer Scientists, II. Retrieved October 23, 2016, from http://www.iaeng.org/publication/IMECS2013/IMECS2013_pp662-665.pdf

[4] Wright, J., Wildung, D., & Nennich, T. (2012). Irrigation Considerations and Soil Moisture Monitoring Tools. Minnesota High Tunnel Production Manual for Commercial Growers, 2nd ser. Retrieved November 4, 2016, from http://hightunnels.cfans.umn.edu/files/2012/11/10-Irrigation.pdf

[5] Chapter 1 – Introduction to evapotranspiration. (n.d.). Retrieved November 04, 2016, from http://www.fao.org/docrep/X0490E/x0490e04.htm

[6] Hatfield, J. L., & Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes, 10, 4-10. Retrieved November 4, 2016, from http://www.sciencedirect.com/science/article/pii/S2212094715300116

 

[7] Kruashvili, I., Bziava, K., Inashvili, I., & Lomishvili, M. (2016). Determination of optimal irrigation rates of agricultural crops under consideration of soil properties and climatic conditions. Annals of Agrarian Science, 14, 217-221. Retrieved November 4, 2016, from http://www.sciencedirect.com/science/article/pii/S1512188716300586