Introduction
Accurate and reliable measurement of leaf wetness and temperature is critical for optimizing crop protection strategies. These parameters directly influence the development of plant diseases, the activity of pests, and overall crop health. This article will delve into the significance of these measurements, exploring various leaf wetness sensor technologies and their inherent limitations, particularly concerning reliability and temperature dependence.
We will then focus on the research-grade INSHU LWS sensor (originally developed by DurUntash Lab LLC, San Diego, CA), examining its capabilities for measuring leaf wetness and temperature. A detailed analysis of a temperature-compensation algorithm specifically designed for the INSHU LWS sensor will be presented. Finally, we will compare the performance of the INSHU LWS sensor with a commercially available PHYTOS 31 sensor, highlighting their respective strengths and weaknesses.
Importance of Wetness and Temperature
Air temperature, relative humidity, and surface wetness are microclimate variables commonly used for assessing crop and fruit loss, and managing disease. Knowing the relationship between weather and the epidemiology of pathogens, some scientists have attempted to decrease pest infestations by modifying the microclimate around crops. The influences of climate change on pest and pathogen dynamics are also well-documented and extensively discussed in the literature.
Leaf surface wetness duration (LWD) is used for monitoring, prediction, and management of diseases in high-value crops. Leaf wetness duration is an important variable in plant disease epidemiology, used to quantify pathogen exposure to free water. Research has shown that bacteria and fungi are closely related to the humidity and wetness of fruit and canopy, and can be used to determine critical times for chemical spray applications.
Fruit surface wetness and temperature are important parameters in pre- and post-harvest crop loss management. Some varieties of fresh-market fruit, such as sweet cherries, tomatoes, and grapes, are sensitive to the presence of moisture on the fruit surface (i.e., fruit surface wetness), which may lead to fruit cracking/splitting and loss of fresh-market value.
For example, sweet cherry fruit cracking caused by rainwater is a major source of financial loss to growers worldwide, especially in the Pacific region of the United States. Sweet cherry crops face the challenge of losses up to 90% in some varieties due to fruit splitting/cracking from seasonal rains prior to harvest. Timely removal of rainwater from the fruit surface requires accurate and real-time monitoring of fruit wetness in the field as part of a crop loss management system.
Surface wetness and temperature of diseased fruits may differ from that of healthy ones. Quantification of fruit surface wetness and temperature could thus provide a promising method for monitoring and predicting diseases/pests in various crops. This is why many disease warning systems rely on leaf wetness as an input to help determine critical times to spray crops against diseases.
Sensor Technology Overview
Due to small gradients in plant canopies, microclimate measurements within canopies require sensitive sensors. In recent years, a wide range of low-cost microclimate monitoring sensors have been developed, which can be used for phenological modeling. In-field microclimate measurements can be carried out using mobile or stationary setups. The microclimate measurements can also be integrated into a wireless sensor network (WSN) to collect information on both spatial and temporal variations.
Leaf wetness can result from dew, rainfall, or irrigation events. Surface wetness duration is often measured indirectly using in-field sensors. A leaf wetness sensor (LWS) measures either the resistance or dielectric constant of a printed grid of interlacing copper wires and relates it to the presence of water on the surface.
Sensor Type Comparison
Although different studies have confirmed the impacts of painting on the sensitivity of resistance grid leaf wetness sensors, they are often deployed unpainted because painting is time-consuming and may result in non-uniformity among the sensors. Currently, there is no standardized method for calculating leaf wetness onset and duration using resistance-type leaf wetness sensors.
Dielectric leaf wetness sensors are a more recent category and detect the presence of water or ice on the sensor surface based on their much higher dielectric constants (80 and 5, respectively) compared to that of air (1). Unlike resistance-type sensors, dielectric leaf wetness sensors come with a surface coating that does not absorb water. This allows for much higher sensor-to-sensor uniformity.
Cylindrical Sensor Analysis
Almost all commercial leaf wetness sensors fall into the category of flat plate sensors that mimic plant leaves. Besides flat plate sensors, cylindrical leaf wetness sensors have also been developed for non-commercial use. Sentelhas et al. (2007) compared these two types of sensors in four different environments. The overall results showed that the cylindrical sensor overestimated leaf wetness duration in a more humid climate and detected wetness earlier than flat-type sensors. Several studies have compared physical and empirical models with visual observations and in-field sensors.
Thermal-RGB Imaging for Wetness
In an experiment, my team and I at the university investigated the feasibility of thermal-RGB imaging for detecting cherry fruit surface wetness level and duration (Osroosh and Peters, 2019). We conducted an experiment in plots of cherry varieties at the university. We used a rain simulator to wet cherry fruits and leaves.
The in-field sensing setup included two custom-built thermal-RGB imagers, a microclimate-measuring unit, and two leaf wetness sensors. We developed a computer vision algorithm to identify leaves and cherry fruits in thermal and RGB images and extract their surface temperatures (Fig. 2). We were able to relate the surface temperature changes to wetness.
Sensor Reliability and Temperature
There are many debates on the reliability of leaf wetness sensors, especially the resistance grid type, as an important input to many pest and disease forecast models. One of the major issues ignored by leaf wetness sensor manufacturers is the so-called "temperature dependence" of leaf wetness sensors. This makes them unreliable for automation projects. This issue is also seen in many other environmental sensors, such as soil moisture and CO2 sensors used in indoor and outdoor agriculture (Blog article: Disentangling Thermal Artifacts: A Novel Approach to Accurate Soil Moisture Measurement).
Temperature dependence means that sensor output changes not only as a result of water or ice presence on the sensor surface, but also due to a change in air temperature. It is interesting to note that almost none of the commercial leaf wetness sensors come with an embedded temperature sensor, which raises the question of how they compensate for the effect of temperature. The answer is, "they don't." This brings us to the issue of unreliability of leaf wetness sensors that often resemble leaves but do not function as one and cannot detect leaf wetness accurately enough.
My academic research at the university and later extensive R&D at my own labs have shown that compensating leaf wetness sensor output for temperature change can significantly increase the accuracy of determining wetness onset and duration. A leaf wetness sensor with temperature-compensated output can be used as a reliable tool in research and automatic systems for fruit and crop loss management.
INSHU Sensor Operation
The INSHU LWS sensor mimics plant leaves and can be used to measure leaf temperature, wetness onset, and wetness duration. It can even quantify the amount of moisture on the sensor surface. The INSHU leaf wetness sensor falls into the category of flat dielectric leaf wetness sensors. It does not require any painting, preparation, or calibration, and all sensors are uniform in terms of wetness readings under the same conditions.
The sensor can detect the tiniest amount of water (condensed or frozen), not only on the sensor surface, but anywhere in its sensing sphere (a few cm from the upper surface; detects fog). This is because the sensor behaves like an RF antenna and creates an electromagnetic field around itself to measure the dielectric permittivity of water or ice. This measurement is later calibrated and related to the surface wetness.
Considering its importance, the INSHU LWS sensor also measures temperature. DurUntash Lab readers and dataloggers use an algorithm that takes air temperature and raw wetness readings and delivers leaf wetness measurements that are reliably independent of changes in air temperature. The temperature compensation means sensor readings are accurate enough to not only indicate the presence of water or ice on the sensor surface, but also quantify the amount. This high level of accuracy makes the INSHU LWS sensor perfect for automation projects.
Data Analysis
DurUntash Lab readers and dataloggers report three numbers with each sensor measurement: 1) probe temperature, 2) leaf wetness (LW; scaled to -3 to 35%), and 3) raw wetness measurement (16-bit resolution). Depending on the installation method and other factors, the readings might exceed 35%, be less than -3%, or never reach 35%. Any value between -3 and 0 is considered noise. A value above zero (0) indicates leaf wetness onset, and the higher the leaf wetness values indicates a higher amount of moisture on the sensor surface. Leaf wetness duration is the time period that above-zero values are recorded. Example real-time temperature and wetness data from an INSHU LWS sensor are plotted in Fig. 3. The data were collected using the DurUntash Lab wireless monitoring system.
Temperature Compensation
To develop an automatic temperature-compensation algorithm, we installed INSHU LWS sensors in direct sunlight and3 studied the effect of temperature fluctuations on sensor readings. The algorithm takes temperature and wetness readings and delivers leaf wetness measurements that are reliably independent of changes in air temperature. This is unique to the INSHU LWS sensor, as all other similar sensors on the market provide leaf wetness readings that cannot be used without user inspection and interpretation of data for consistency and temperature dependence.
As shown in Fig. 3, even though temperature fluctuations exceeded 30°C on some days, the leaf wetness measurements are consistent and reliable. The sensor is very sensitive to moisture and can detect a single drop of water on the sensor surface (or even heavy fog in some cases). This amount of moisture is enough to increase the readings to values above 5%. Values below 0% that fluctuate are not an indication of wetness, but the result of noise and extremely high temperatures (> 40°C). (With the updated INSHU algorithm, noise is effectively filtered and therefore no longer visible.)
We strongly recommend using the INSHU LWS sensor with one of our readout devices or dataloggers to unlock its full potential. If you are using your own readout device or data acquisition systems, the sensor output will be temperature and raw voltage. With non-DurUntash dataloggers, you will need to convert raw voltage readings to leaf wetness, perform temperature compensation, and establish a wetness threshold for your setup. Raw wetness readings depend on the readout device (custom-built electronics or compatible dataloggers) that the INSHU LWS is connected to and the resolution of the analog-to-digital converter used. As an example, the ADS1115 ADC (16-bit resolution) will report numbers that may range from 10,000 to 15,000 when set for 2x gain.
Sensor Performance Comparison
Leaf wetness data collected using the INSHU LWS sensor and PHYTOS 31 (Decagon / METER Group, Pullman, WA) sensors are depicted in Fig. 4 and Fig. 5, respectively. The INSHU LWS was installed in the field and captured both rainfall events and dew formations. The INSHU LWS embedded temperature sensor recorded a temperature change of 8 to 41°C. The PHYTOS 31 was installed in a cherry orchard (in the canopy) where the temperature fluctuated from 24 to 31°C during our experiments. The PHYTOS 31 does not provide temperature output, so we used a microclimate unit to measure air temperature (Osroosh and Peters, 2019).
As shown in the graph (Fig. 4), the INSHU LWS sensor's leaf wetness baseline is a straight line (zero). The PHYTOS 31, on the other hand, has trouble distinguishing between ambient temperature change, noise, and leaf wetness. As a result, the PHYTOS 31 generates many false positive signals, detecting leaf wetness when no moisture is present on the sensor surface.
Key Sensor Requirements
Leaf wetness and temperature are paramount in managing fruit and crop losses. While "leaf wetness sensors" offer the potential to monitor these critical parameters, many commercially available options suffer from significant reliability issues. The primary concern is their pronounced temperature dependence. Furthermore, a standardized method for calculating leaf wetness onset and duration using resistance-type sensors remains elusive.
Based on these considerations, a truly "reliable" leaf wetness sensor should possess the following characteristics:
Dielectric (capacitive) technology: This allows for using a pre-coated sensor.
Pre-coated sensor surface: Ensures consistent and accurate readings due to increased sensor-to-sensor uniformity.
Temperature output or compensation: Enables accurate wetness readings by accounting for temperature variations.
Plant-like properties: Ideally, the sensor should mimic the physical and thermal properties of actual leaves, though this presents a significant engineering challenge.
The INSHU LWS sensor stands out as the only commercially available sensor that currently meets all these criteria, offering a promising solution for accurate and reliable leaf wetness monitoring in agricultural applications.
Citation
Osroosh, Y., 2021. Why Most Leaf Wetness Sensors Fail. EnviTronics Lab, Apr 22.
References
Osroosh, Y., Peters, R.T., 2019. Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager. Computers and Electronics in Agriculture, 157: 509-517.
Sentelhas, P.C., Gillespie, T.J., Santos, E.A., 2007. Leaf wetness duration measurement: comparison of cylindrical and flat plate sensors under different field conditions. International Journal of Biometeorology, 51: 265–273.
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