Irrigation automation based on soil electrical
conductivity and leaf temperature
Noble Abraham
*, P.S. Hema, E.K. Saritha,
Shinoj Subramannian
Kelappaji College of Agricultural Egnineering and Technology, Kerala Agricultural University, Tavanur, Malappuram, Kerala 679573, India
Accepted 21 September 1999
Abstract
Two automated drip irrigation systems: one based on soil electrical conductivity and the other based on leaf±air temperature differential were developed and tested for Okra (Abelmoschus esculentus). Different sensors were evaluated for monitoring the soil moisture content based on electrical resistance variation with moisture content. The sensor with washed sand as porous medium was found to be the most ef®cient one for the study area. A low cost, commercially available button type thermistor was used as the leaf and air temperature sensors. The amount of water applied per day, leaf±air temperature and soil moisture content were monitored during the study period. The systems maintained the designed soil moisture content and air±leaf temperature differential through out the study period.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Irrigation automation; Moisture sensor; Leaf±air temperature differential
1. Introduction
Where rainfall is inadequate, farmers have always sought ways to supply crops with the water necessary for its development. The recent irrigation techniques introduce automated irrigation using sophisticated equipments to supply water to the plant as soon as they need it. Automated irrigation systems can increase crop yields, save water usage, energy and labour costs as compared with manual systems (Mulas, 1986). Automated irrigation has a number of advantages including greater precision, more efficient use of water and reduction in human error (Castanon, 1992). It is very useful, particularly in
*Corresponding author. Tel.:91-494686090.
humid areas where unpredictable and unevenly distributed summer rainfall disrupts fixed irrigation schedules. Automated irrigation system also facilitates high frequency and low volume irrigation.
Automatic irrigation systems presently available are costly and are not adopted by most of the Indian farmers. Therefore, appropriate low cost technology has to be developed to facilitate high water use efficiency. A study was therefore conducted to evaluate the soil electrical conductivity and leaf±air temperature differential as indicators for irrigation automation. Relationships between soil moisture content and electrical resistance, and soil moisture content and leaf±air temperature differential were established. Based on these observations, two automated irrigation systems: one with soil electrical conductivity and the other with leaf±air temperature as indicators for irrigation automation were developed. Testing and performance evaluations of these automated systems were conducted.
For irrigation scheduling, there is always a need for reliable methods for measuring soil and plant water status. The most important and basic component of a measurement system is the sensor. The efficiency of various management decisions depends on accurate measurements, which in turn depends on the accuracy of the sensor.
Shull and Dylla (1980) suggested the use of gypsum resistance blocks as soil moisture sensor. On larger fields for extending the soil moisture sensing area, a network of gypsum resistance blocks was made by connecting them in series and in parallel with a resistance range the same as that provided by one block. Usually in border irrigation automation, the pneumatic sensors are being superseded by electronic water sensors due to the blocking of air transmission line by debris. Alharthi and Lanje (1987) suggested a method of assessing the water saturation by the measurements of composite dielectric constant. Tension measurements by tensiometers are generally limited to matric suction values of below one atmosphere. They do not satisfactorily measure the entire range of available moisture in all soil types. (Michael, 1995). The resistance based sensors are simple and the signal output can be directly fed to the control systems.
Cuming (1990) developed an irrigation control system, which includes a soil moisture sensor that controls the common lines of various irrigation systems. A timer is activated whenever the soil moisture sensor placed in the root zone allows it to be watered. Frankovitch and Sarich (1991) developed an automatic plant watering system consisted of an electronic switching system that controls pumping time. The flow rate of water is controlled by a valve system.
allowing sunlit leaves to warm above ambient air temperature. Irrigation scheduling based on the canopy air temperature differential has been suggested by Walker and Hatfield (1979). Jackson (1982) found that an ideal irrigation scheduling technique should use the plant as the indicator of water stress, since the plant response to both the aerial and soil environments. The use of canopy temperature to detect water stress is based on the principle that water lost through transpiration cools the leaves below the temperature of the surrounding air under well-watered conditions. Throssell et al. (1987) reported that the plant canopyambient air temperature difference is a good indicator of the water status of a plant. According to Kadam and Magan (1994), the canopy air temperature difference is related to leaf water potential. Also, Bhosale et al. (1996) reported that the canopy air temperature differential is a good indicator of water status of the plants.
Different types of sensors are used for measuring canopy temperature. Ehrler (1973) used thermocouple embedded in cotton leaves to determine leaf temperatures. Saha (1984) used infrared radiation thermometer for measuring canopy temperature for monitoring plant stress from aircraft. Ahmed and Misra (1990) described the method of measuring leaf temperature with thermocouple.
Jackson et al. (1977) suggested the possibility of development of a totally automated irrigation system in which instruments monitor the canopy temperature of plants for signs of water stress and signal devices that automatically provide required amounts of irrigation water. Wanjura et al. (1995) developed an automated drip irrigation system based on threshold canopy temperature. Irrigation was applied only when average canopy temperature exceeded pre-determined threshold values. The length of irrigation cycles was shortest and amount per irrigation event was highest for all threshold temperatures during the early growth stage as canopies were small, and warm bare soil contributed to measured canopy temperature.
The experiments conducted and materials used in this study are described below.
2. Materials and methods
The experiment was conducted at Malappuram district of Kerala, India, situated at 108520
300 0
North Latitude and 768East Longitude. The soil at the site was sandy loam. Two experimental plots (Plot 1 and Plot 2) were selected, each with an area of 2 m2 m. The crop planted was Okra (Abelmoschus esculentus) of variety Arca Anamika for which one of the main planting season is February±March. It has an excellent rooting pattern and good canopy, with moderately strong and thick leaves. The automated irrigation system based on electrical conductivity of soil was installed in Plot 1 and the other system based on leaf±air temperature differential was installed in Plot 2. The system consisted of sensors, controller and solenoid valve to regulate the irrigation input. The layout of the plot is shown in Fig. 1.
in between electrode plates were evaluated. Among the five sensors, four of them had brass plate of size 30 mm25 mm as electrodes with a gap of 10 mm between them. Brass was selected as electrode after comparing the performance of stainless steel, copper and brass. The porous media used were soil at the site, washed sand, sponge and nylon for first, second, third and fourth sensors, respectively. A gypsum block was the fifth sensor used for evaluation. All the five sensors were embedded in the soil at a depth of 50 mm and the soil was irrigated to saturation. The soil moisture content and corresponding soil electrical resistance were then monitored till a nearly constant moisture content was reached. Four trials were done leaving a gap of 4 days. The selection of appropriate sensor was made on the basis of the uniformity of soil moisture content electrical resistance relationship in all the four trials. The resistance corresponding to the field capacity of soil was also determined.
The relationship of leaf±air temperature differential with soil moisture content was established and was accepted as an indicator for irrigation scheduling. In order to sense the leaf and air temperature and for converting it into a signal acceptable to the switching circuit, a commercially available button type thermistor was used. Thermistors are extremely delicate components whose effective surface area in contact with the leaf is very small. For easy and quick flow of heat, thermistor was attached in between the leaf and an aluminium foil of 20 mm10 mm size. In order to revent the effect of direct solar radiation, the sensor was placed on the underside of the leaf. Small holes were provided on the aluminium foil in order to aid smooth transpiration. A similar button type thermistor was used as air temperature sensor, which was hung freely within the microclimate of the plant. The circuit used for automation based on electrical conductivity of soil is shown in Fig. 2. The electrode for sensing the soil moisture was placed at a depth of 50 mm from the surface within the root zone of a plant at the centre of the plot. Variation of moisture in soil causes variation in electrical resistance across the electrode of the sensor. The electrical signal obtained by variation in electrical resistance is processed by the circuit and operates the relay contacts connected to a 12 V dc operatednormally closedsolenoid valve. When the soil gets dry and its resistivity increases, the circuit open the valve and water flows to the plants. As water content in soil reaches the required level set by the variable resister VR1, the solenoid valve is closed. A 9 V dc supply powers the circuit. The field capacity of the soil at the site was found to be 15%. The electrical resistance corresponding to this moisture content was 33 kOfor the selected sensor. This resistance was set in the switching circuit, so that, when moisture content decreased below field capacity, the system switched on and when the field capacity of the soil was reached during wetting up, it switched off. Thus, the moisture content was always maintained around field capacity level. To prevent the vibration of the relay contacts at the switching point, a time delay for switchingonthe circuit was given.
2.1. Automation based on leaf±air temperature differential
When the temperature of the leaf changes with the soil moisture content, the resistance of the thermistor RTH1, (attached to the leaf) varies. Thermistor RTH1, along with the variable resistance VR1, constitutes a potential divider across the supply, resulting in a voltage at pin 2 of the Op-amp IC1 741. This voltage rises as the temperature decreases. Thermistor RTH2, (exposed to atmosphere) along with the variable resistance VR2, provides a reference voltage at pin 3 of IC1. When the voltage at pin 2 rises above that at pin 3, IC1 switchesonand voltage appears at the output pin 6. The temperature at which this happens is pre-set by adjusting variable resistances VR1 and VR2. In order to make the sensors more sensitive to temperature changes, thermistors having different resistance values were used. When the required temperature differential set by VR1 and VR2 is reached, the output from IC2 turns transistor T1 CL100on, and thus, it drives the relay. IC3, an NE555 is used to have a time delay for operating the relay even after the pre-set temperature difference is reached. Otherwise, sudden fluctuations in the temperature of either atmosphere or leaf due to wind may affect the operation of the system. Adjustment of VR3 can vary the time delay, if needed.
The relay contacts were connected to a 230 V ac operatednormally closed solenoid valve whose input was connected to an overhead tank and output to the drip irrigation system installed at the field. Thus, when the plant is stressed, the temperature of leaf increases with respect to atmosphere, the valve is opened and water flows to the plants. Then the plant leaves begin to cool and when the temperature reaches the pre-set value, the valve stops the flow of water. The circuit was powered by 9 V dc supply. The switching circuit used for automation based on leaf ±air temperature differential is shown in Fig. 3.
In the humid climatic conditions of Kerala, Okra showed a leaf temperature around 48C below that of atmosphere under well watered conditions. It was found to be going upto 68C in certain days. This differential became 08C at extremely dry conditions. When the leaves stared drooping, an air±leaf temperature differential of 28C or less was observed. So the control circuit was adjusted such that it keeps on at an air±leaf temperature differential of 38C. The selection of this air±leaf temperature differential was also based on the sensitivity of the sensor used in this study.
To evaluate the performance of the two automated systems, the soil moisture content was measured from each automated plot, three times a day, i.e. at 8.30 am, 12.30 and 3.30 pm. At the same time, air temperature and leaf temperature were also measured from the Plot 2. The amount of water applied was also noted using two water meters. Moisture contents from the two plots were determined by the gravimetric method. The yield and dry matter content obtained from each plot were determined.
3. Results and discussion
The measurement of resistance in the field using gypsum block showed that when the polarity across the electrodes changed, the resistance readings had considerable variations. The performance curves of gypsum block for the four replications are shown in Fig. 4. The sensor having soil in the field itself as the porous medium showed the same relationship between moisture content and electrical resistance in the first and second trials. During the third trial, it showed a slight variation from the trend. The fourth trial showed considerable variation. This may be due to the presence of chemicals in the soil. The performance curves for four trials are shown in Fig. 5. For the sensors having sponge
and nylon as porous medium, four trials showed different trends in the relationship between soil moisture content and electrical resistance. The variation may be due to the moisture retention properties of these materials are different from that of the soil. The performance curves forspongeandnylonare shown in Figs. 6 and 7, respectively. In all the four replications, the sensor having washed sand as porous medium showed a constant trend in the relationship between soil moisture content and electrical resistance. This is
Fig. 5. Performance of soil in the plot as porous medium.
due to washed sand is less susceptible to chemical changes and the soil moisture properties of the washed sand are probably similar to that of field soil. The performance curves forwashed sandare shown in Fig. 8. Based on the above results, the sensor with washed sand as porous medium was selected and used as the soil moisture sensor in the present study.
Fig. 7. Performance of nylon as porous medium.
The system based on electrical conductivity was tested during the month of February± April 1998. A plot of soil moisture content verses time for 12 days during the matured stage of the crop is shown in Fig. 9. During the matured stage, maximum fluctuation in soil moisture content of the root zone is expected due to increased evapotranspiration. It can be seen that the moisture content was maintained nearly constant throughout the period within the range 14±17%, that is around field capacity of the soil. On 9th day, a moisture content of 19% is observed as the moisture content was happened to be taken during the time of irrigation.
About 1 week after the installation of the sensor, some deposits were found to form on the electrode plates that reduced the electrical conductivity between the electrode plates. These deposits may be due to the polarisation of certain ions present in the soil. The same trend was found immediately after the addition of fertilisers to the soil.
The system based on leaf±air temperature differential was also tested in the same period. The soil moisture in this case was maintained between 10% and 13% throughout the study period. The moisture content for 12 days during the matured stage of the crop is shown in Fig. 10. Here upto a moisture content of 10%, the differential was less than 28C, upto 14% moisture content, the differential was about 38C and beyond that it was above 48C. This shows that there is a distinct variation in leaf±air temperature differential corresponding to soil moisture content for the crop Okra.
The system was found to maintain the pre-set value of leaf±air temperature differential and the leaf temperature was maintained 38C below the atmospheric temperature. At this level the moisture content was less than field capacity and the plant was subjected to certain level of moisture stress. The relationship of soil moisture content with the leaf ±air temperature differential is shown in Fig. 11. It shows that leaf±air temperature differential has a direct relationship with soil moisture content.
The volume of water applied during this period as irrigation is shown in Fig. 12. It can be seen that more amount of water was applied in the first plot where system based on soil resistivity was installed and a higher moisture content was maintained. The yield and the drymatter content was also more in the first plot. On days 3, 5, and 9, there was rainfall and the irrigation applied was less. The performance of both the systems were
Fig. 10. Soil moisture status in Plot 2 (automation based on leaf±air temperature differential).
satisfactory and can be adopted for irrigation scheduling. The systems are simple and can be easily maintained by farmers.
Application of fertilizer or chemical change, the resistance moisture content relationship and therefore calibration of sensor is required after adding fertilizers or chemicals. Such variations are not required for the system based on leaf±air temperature differential. However, the sensor need to be changed to new leaves as the plant canopy develops.
4. Conclusions
The sensor with brass plate as electrode and washed sand as porous medium showed nearly a constant trend in the relationship between resistance and soil moisture content in all trials.
The automated systems based on soil resistance was found to be working efficiently without frequent supervision and maintained the pre-set moisture content in the root zone. The automated system based on leaf±air temperature differential maintained the pre-set leaf±air temperature differential throughout the study period.
References
Ahmed, M., Misra, R.D. (Eds.), 1990. Manual on Irrigation Agronomy. Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi, pp. 121±122, 272±282.
Bhosale, A.M., Jadhav, A.S., Bote, N.L., Varsheya, M.C., 1996. Canopy temperature as an indicator for scheduling irrigation for wheat. J. Maharashtra Agric. Univ. 21 (1), 106±109.
Castanon, G., 1992. The automation irrigation. Maquinas of Tractors Agricolas 3 (2), 45±49.
Clark, R.N., Hiler, E.A., 1973. Plant measurements as indicators of crop water de®cit. Crop Sci. 13, 466±469. Cuming, K.D., 1990. Irrigation control system, United States Patent US4, 934, 400, 5 pp.
Ehrler, William L., 1973. Cotton leaf temperatures as related to soil water depletion and meteorological factors. Agron. J. 65, 404±409.
Frankovitch, D.J., Sarich, J.I., 1991. Automatic plant watering system, Canadian Patent application, 16 pp. Gates, D.M., 1964. Leaf temperature and transpiration. Agron. J. 56, 273±277.
Jackson, R.D., 1982. Canopy Temperature and Crop Water Stress, Advances in Irrigation, vol. I, Academic Press, New York.
Jackson, R.D., Reginato, R.J., Idso, B.B., 1977. Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resour. Res. 13 (3), 651±656.
Kadam, J.R., Magan, S.S., 1994. Irrigation scheduling with thermal infrared remote sensing inputs: a review. J. Maharashtra Agric. Univ. 19 (2), 273±276.
Kramer, P.J. (Ed.), 1969. Plant and Soil Water Relationships. McGraw-Hill, New York.
Michael, A.M. (Ed.), 1995. Irrigation Theory and Practice. Vikas Publishing House Pvt. Ltd., NewDelhi, pp. 490±501.
Mulas, P., 1986. Developments in the automation of irrigation. Colture Protelte 15 (6), 17±19.
Saha, S.K., 1984. Remote sensing of crop evapo transpiration using plant canopy temperature. Saha et al. (Eds.), Proceedings of the Seminar on Growth Condition and Remote Sensing. IARI, New Delhi, India. Shull, H., Dylla, A.S., 1980. Irrigation automation with a soil moisture sensing system, Trans. ASAE, Vol. 23
pp. 649±652.
Tanner, C.B., 1963. Plant temperatures. Agron. J. 55, 210±211.
Throssell, C.S., Carrow, R.N., Milliken, G.A., 1987. Canopy temperature based irrigation scheduling indices for Kentucky Bluegrass Turf. Crop Sci. 22, 126±131.
Walker, G.K., Hat®eld, J.L., 1979. Test of the stress degree day concept using multiple planting dates of red kidney beans. Agron. J. 71, 967±971.
