Precision agriculture is experiencing a huge number of scalable infrastructure changes starting from indoor facility vertical farming to sensor enabled automated equipment control used on traditional farms.
Intelligent systems regulate crop insecticide application, monitor irrigation, control boom heights on harvesters and tillage equipment, monitor silo level and remote tank, and more. These systems basically enhance farming efficiencies to enable obtaining higher outputs and crop yield in order to feed an ever-growing world population.
Recent liberalization trends in genetic manipulation of plants and herbal medications have facilitated a budding of the medical hemp CBD oil industry for nausea and pain treatment in oncology management. Natural plant substances such as berberine have been established to benefit type II diabetics.
The need to develop improved management of crop control monitoring grows as more benefits are discovered for naturally occurring compounds. These include characterization of plant canopies, automated equipment controls and precise application of water and fertilizer.
Agriculture Meets Technology — Phase One
Renowned equipment efficiency benefits have been implemented using ultrasonic level sensors in order to control the spray boom heights of insecticide and harvesting equipment by major farm equipment manufacturers. These types of devices use optimized ultrasonic air transducers for monitoring spray distance from a delivery nozzle to plants to enable more accurate metering of insecticide or fertilizer application. Ultrasonic sensors are the preferred implementation due to their lower cost of ownership, potential to function in a variety of applications, and ease of setting adjustability such as sampling rate. For tank monitoring, spray distance measurement, boom height control, collision avoidance and inclination level applications, rugged ultrasonic sensors have proven invaluable. Sensors such as the MassaSonic® PulStar® Plus with broadly controllable sampling rates and improved echo processing have proven useful for a wide range of these industry applications.
However, by its nature, agricultural operations such as irrigation systems and field storage locations also call for remote monitoring capabilities in order to control water distribution systems. Ultrasonic sensors like the low power consuming PulStar TTL or battery operated wireless SonAire® have proven value in system deployments where solar trickle charging or remote battery power provides the only economical means of power.
Agriculture Meets Technology — Phase Two
Although equipment control and monitoring improvements via sensors are well known, use of sensors for documenting growth rates and crop health continues to be an evolving area. Speciality crops, like those grown in controlled environments - such as greenhouses, vertical farming or nursery settings produce a great yield with less resources.
They provide a controllable growth setting suitable for medically valuable crops that need regulation, distribution monitoring and optimization of the therapeutically active compounds. Sensors can be utilized by plant breeders to accurately monitor plant canopies, growth rate and hence, the multifactor interactions of fertilizer and water application. The remainder of this article examines the effectiveness of using an ultrasonic sensor as a means of non-destructive, non-contact canopy mapping. The research was performed on corn plants using Massa Products MassaSonic sensors and presented at the ASAE/CSAE meeting by Researchers at Iowa State University, University of Idaho and the USDA(I).
Abstract - Ultrasonic Sensing for Corn Plant Canopy Characterization
Non-destructive measurement of crop growth stage, height and canopy development may be useful for more efficient crop management practices. In this study, ultrasonic sensing technology was analyzed as one approach for the characterization of corn plant canopy. Ultrasonic echo signals from corn plant canopies were gathered using a lab-based sensor platform. Echo signal peak features were extracted from multiple scans of plant canopies. These features included scan number, peak amplitude and time of flight. The growth stage of each plant was estimated based on the number of leaves detected. A leaf-signal interaction model was produced to predict which parts of leaf surfaces will result in echo signals detectable by the sensor. The research aimed at developing a sensing system which extracted information from an ultrasonic sensor that could be used for a wide range of sensing operations in precision agriculture.
Methodology Summary — Experimental Design & Procedure
Ultrasonic echo signals from corn plant canopies were collected using a lab-based sensor platform comprising of a motion control system on which an ultrasonic sensor (equivalent current model MassaSonic PulStar 95 kHz) was mounted above and perpendicular to the soil surface. The sensor was moved in the horizontal plane using a computer control and lead screw. The sensor was designed to sense depth in a range of 0.3 and 4 m.
Experimental Design and Procedure
Two sets of 10 corn plants at V6 and V9 growth stages respectively were placed on the imaging stage one plant at a time and then scanned with the ultrasonic sensor. The ultrasonic sensor was passed over the individual plants at a velocity of 0.57 cm/s. The echo monitor output signal from this sensor was fixed to a data acquisition system.
The signal was a diagnostic output that included the transmit pulse and the reflected signals from the target. An oscilloscope was employed for observing the signals during collection. Manual measurements were taken of the maximum plant heights, corn plant collar heights and maximum heights of each leaf.
The signal resulting from reflections from the target was an analog voltage ranging from 2.4 V to 5.3 V. This signal was sampled at 100 kHz by the data acquisition system and 1400 samples of the peak detection signal voltage were acquired for each scan. Ultrasonic pulses were introduced at a frequency of 2 Hz and the frequency of the ultrasonic waveform was 95 kHz. Based on the top projected canopy area of a plant, between 252 to 360 scans were attained of individual plants as the sensor was passed over the plant canopy at a speed of 0.57 cm/s. For this lab based environment, this speed was considered to be appropriate for the sensor to sufficiently characterize the plant canopies.
Two aspects of ultrasonic sensing were analyzed. The heights of corn plant leaves were first estimated using ultrasonics echo signals and subsequent signal processing. This was then followed by investigating the interaction between corn leaf surfaces and ultrasonic signals to improve the understanding of which surfaces could be expected to return a signal to the sensor.
Leaf Height Estimation
Ultrasonic signals were processed to detect the start of echoes that were reflected from the object in the sensor field of view (FOV) using a fixed threshold at 2.7 V. Time of flight of each detected peak was transformed into a height estimate using the following equation:
L is the distance between ultrasonic sensor to the ground, 2.105 m
c is speed of sound in air, 20.064 m/s
t is the round-trip time of flight
T is the air temperature in degrees C
For each echo peak, a peak feature vector composed of features such as time of flight, height and scan number were calculated. Peak feature vectors were clustered together according to scan number and height similarity in order to identify individual leaves of the corn plant. This was followed by estimating the mean height of the clustered data of individual leaves. The number of leaves detected helped to estimate the growth stage of each plant.
Leaf Signal Interactions
The interaction between ultrasonic signals and leaf angle was investigated based on the basic principles of sound wave propagation and reflection: A sound beam incident upon a reflective surface will be reflected at an angle equal to the incident angle (GBSB, 1996; Figure 1).
Figure 1. The law of reflection states that reflection angle, θr for a beam incident upon a reflective surface is equal to the incident angle θi.
The beam angle of the ultrasonic sensor used in this study was +/- 4° . Based on the wave reflection principles, those sound waves which would be detected by the sensor would propagate from the sensor in a cone whose size was determined by the beam angle. A portion of the sound energy is reflected from the leaf surfaces when the wave comes in contact with the leaf surface. In order for the reflected wave to be detected by the sensor, it is essential for the surface to be oriented so that the wave is reflected back to the sensor within the beam angle. Thus prototype leaf models with simple mathematical forms were developed. The tangent line along a leaf surface model was calculated where the slope of the tangent line is the derivative of the leaf model function, y (Figure 2). Using this tangent line, the tangent angle 90 relative to the horizonal at (xo, yo) point was calculated using the following equation:
As the beam angle of the sensor is 4° , any point on the surface that has a tangent angle, 0 less than 4° would reflect back the signal to the sensor receiver, as shown in Figure 2. This model helped determining the regions of prototype leaf surfaces that would create detectable echoes by the sensor.
Figure 2. Tangent line of a leaf surface at point (X0, Y0).
Leaf Height Estimation
Detected echo peaks were changed into estimated height and plotted against the scan number, as shown in Figure 3. In many cases, it is possible to observe the number of visible leaves and their heights. The leaf shapes reflected in the scatter plot were more flat than the original plant because multiple signals were received from the same point due to the large FOV of the sensor. Erect leaves were not detected by the ultrasonic sensor as these leaves reflected the ultrasonic signal away from the sensor. Leaves in the whorl tend to be more erect, so they tended to not reflect the signal back to the sensor. Echoes were only detected if the leaves bent enough to be roughly perpendicular to the sensor. Leaves that were completely extended tended to be more perpendicular to the sensor and appeared in a lot of scans and were typically identifiable as distinct leaves:
Figure 3. (a) Example scatter plot of leaf height estimates from ultrasonic scans as a corn plant in photo (b) was scanned from left to right.
Other echoes after the first received echo may also be present. These could be due to the sound bouncing off multiple reflectors and back to the transducers, or echoes from targets beyond the first target.
The height measurements of individual leaves using the ultrasonic sensing system for 18 plants were usually correlated with the manually measured heights with r2 =0.56 (Figure 4). Data from two V6 growth stage plants were removed as outliers because of unreasonable height scale due to the bad condition of a plant or an error in the lab experiment and data collection. Figure 5 shows the regression line of leaves height from eight V6 growth stage plants with r2 = 0.87. For the V9 growth stage, estimated individual leaf height from all 10 plants were regressed onto actual leaf height and the correlation of determination, r2 was 0.41 (Figure 6).
Figure 4. Estimated individual leave heights regressed onto manually measured leave height for 18 plants.
Figure 5. Estimated individual leave heights regressed onto manually measured leaves height for 8 plants for V6 growth stage.
Figure 6. Estimated individual leaves heights regressed onto manually measured leaves height for 10 plants of V9 growth stages,
Leaf Signal Interactions
As a theoretical development to improve the understanding of the interaction of the sensor with idealized models of plant leaves, two possible leaf shapes were modeled, a straight line at a slope shape and a parabolic shape. For a parabolic shape, the number of points detected as sensor traveling along a convex curve y= mx2 increased exponentially as the m parameter increased from -0.350 to 0. For a straight line at a slope shape, the ultrasonic signal will be reflected back to the sensor if the slope is less than 0.70 (Figure 7).
Figure 7. Two possible leaf shape models, (a) parabolic leaf shape and (b) straight line slope shape. For the parabolic leaf shape, only top surfaces were detectable and for straight line at a slope shape, all parts can be detected if the slope is less than 0.70.
The echo peak distribution is considerably affected by leaf angle. More studies are needed to retrieve a better correlation between ultrasonic measurements and other useful plant characteristics in filed crop studies such as cumulative leaf area index, collar height and plant canopy density. The study only dealt with a basic leaf plant model. However, full plant model development may be useful to improve the understanding of the relationship between ultrasonic signal and spatial plant canopy characteristics.
This information is extremely useful in analyzing the expected ultrasonic signal that would reflect back to the receiver as the sensor scans through a plant canopy. With a full plant model, it could also possible to establish the correlation between the measurement of plant height using actual manual measurements and this mathematical modeling.
In summary, simple spatial characteristics of plant leaves in relation to their growth stage, height and angular responses of ultrasonic reflection were studied. The estimates of individual leaf heights from ultrasonic measurements of separate leaves using time of flight calculation method were positively correlated with manually measured heights of the corn species. The reference study describes these findings.
For crop canopies, characterization of leaf area, angle and leaf orientation may be factors that can be used to determine plant growth and health (2). By employing cost effective ultrasonic monitoring, plant breeders may use these characteristics to down select optimized phenotypes in order to provide, in the following years, an output with improved yield, therapeutic performance or lowered costs (3).
(1) From "Ultrasonic sensing for corn plant canopy characterization" by S. A. Aziz, B. L. Steward, S. J. Birrell, D. S. Shrestha, and T. C. Kaspar. Presented at the 2004 Annual International Meeting of the American Society of Agricultural and Biological Engineers as ASABE Paper No. 041120. St. Joseph, Mich.: ASABE. Used with permission.
If your article will include the paper in a formal bibliography or references list, then the paper can be cited in the text as: "From Aziz et al. (2004). Used with permission."
(2) From "Farm View Uses Al To Feed a Growing Planet" by L. Schmitmeyer. The Link, the magazine of Camegie Mellon University's school of computer science. Winter 2016 issue 10.2
(3) From Sensors 2011, 1 1, 2177-2194 "Ultrasonic and LIDAR Sensors for Electronic Canopy Characterization in Vineyards: Advances to Improve Pesticide Application Methods" by Jordi Llorens, Emilio Gil, Jordi Llop & Alexandre Escola.
This information has been sourced, reviewed and adapted from materials provided by Massa Products Corp.
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