Over the last decade or so, I have read various publications on using RGB and multispectral imaging to determine canopy cover percentage and relating it to crop coefficients. In almost all of those papers, the authors whished for an automated system that would exploit this concept!
Driven by my passion for applied research, and sensors and automation, I decided to automate the process by developing a low-cost all-in-one multimodal imaging system (thermal + multispectral) and embedding required crop models into the system. This would be appropriate for both indoor and outdoor grow operations. The focus of this short article is not the system itself, but how one can combine imaging and microclimate data (radiation, RH, air temp, wind speed) to estimate crop water use of plants grown in pots, vegetables or row crops.
The same [automation] concept can also be implemented in other imaging platforms such as unmanned aerial vehicles (UAVs) by using embedded systems and doing some coding, or even on a smartphone. However, a low-cost handheld multimodal automatic imager would be more appropriate for research and small scale farming. This removes the latency and allows for better decision-making.
Evapotranspiration-based Irrigation Scheduling
Currently, the primary approach for monitoring plant/soil water status and irrigation scheduling is to use moisture sensors. The question is if there is an alternative to measuring substrate moisture using sensors to determine the water needs of plants grown in pots?
A great alternative is ET-based irrigation scheduling or irrigation based on potential crop water use (ETc), which is usually estimated daily using an evapotranspiration model and a crop coefficient (Kc). Unfortunately, crop coefficient values (or curves) are not established for most crops especially those usually grown in pots. The solution is to develop your own “crop coefficient curve” (just once) during a growing cycle and use it for future measurements. Here's how you can determine Kc for your crop:
Choose a number of plant pots (representative of the rest).
To determine the actual water consumption (ETa = actual ET) of your plants, weigh the pots daily for a change in water content divided by the area of the top of the container. Your plants must be maintained well-watered throughout the experiment so ETa and [potential] crop ET, which is estimated, are the same.
Use microclimate data (radiation, relative humidity, air temperature, wind speed) and the Penman-Monteith evapotranspiration model (Allen et. al, 1998) to calculate the daily potential water use (ETr or ETo = potential ET for alfalfa or grass).
Divide ETa by ETr to calculate your daily Kc. Plot all the values and you will have a crop coefficient curve as a function of days from planting.
If you need more accurate crop ET estimations that are a function of actual crop growth rather than the number of days from planting, take the additional steps described below:
Everyday take a top-view picture of the plant pots and determine the ground cover percentage (C, %). This step requires a RGB or multispectral (preferred) camera some image processing knowledge (Figure 1).
Plot C values against the crop coefficient values and develop a ground cover function for your plants, which would be a polynomial equation (Kc = a x C^2 + b x C + m).
In the future, to determine the daily water need of your plants you will only need a top-view picture of your plants and microclimate data. Please note that the same method can be used for estimating the water use of vegetables and row crops.
Figure 1. Canopy coverage (%) is converted to crop coefficient (Kc) using a polynomial equation.
Automated Estimation of Crop ET
To make the process painless, I have embedded crop coefficient functions (Bryla et al., 2010) for a few crops including lettuce, garlic and tomatoes in my handheld all-in-one multimodal imaging system (Model: BINA Pro, DurUntash Lab, San Diego, CA) (Osroosh, 2021). Whenever I need to measure crop ET, I just plug in a commercially available microclimate unit (MA-4100, Decagon Devices, Pullman, WA) into the system's SDI-12 port (Figure 2), and take a top-view picture of my plants. The BINA Pro automatically calculates the crop ET and other useful parameters for me, geotags and timestamps the results and saves them in a 'csv' file.
Figure 2. The all-in-one imaging system automatically estimates daily crop ET (ETc) using a top-view image of the crop to estimate crop cover (C, %), a polynomial equation that converts C to crop coefficient, and the Penman-Monteith ET model. Microclimate data is obtained from an external microclimate unit that is plugged into the BINA Pro SDI-12 port.
Reference
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56. FAO, Rome, Italy, 300 pp.
Bryla, D.R., Trout, T.J. and Ayers, J.E., 2010. Weighing Lysimeters for Developing Crop Coefficients and Efficient Irrigation Practices for Vegetable Crops. HortScience, 45: 1597-1604.
Osroosh, Y., 2021. Low-cost thermal-spectral imagers with embedded computer vision capabilities and crop models for automated agricultural monitoring and management. EnviTronics Lab, San Diego, CA, Aug 17.