Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

Conventionally, insecticide efficacy tests conducted in the field include several treatments of the chemical applied at different rates and compared to an untreated check. Acaricides with varying toxicity profiles against instar and adult stages of TSSM are assessed to determine whether the damage caused by them could be reduced by the chemical treatment. TSSM samples are collected and brought to the laboratory where they are examined under the microscope and the varying stages of TSSM are counted and recorded. It is critically important to take adequate samples of the host plant to determine the damage in each treatment and differentiate them with statistically acceptable precision. The number of samples that are required to differentiate the treatments from each other depends upon the distribution profile of the organism. Highly patchy distribution of the TSSM leads to a considerable amount of variation between sample areas, and many plants must be sampled in order to ensure reproducibility of the population estimates. However, budget, manpower, time and statistical precision are important factors that impinge upon sampling techniques. It behooves the researcher to allocate the available resources optimally to conduct sampling with the least cost but with most precision.

Entomologists in lieu of counting TSSM stages visually score the damage based upon a scale of no damage to varying levels of damage. For instance, some researchers proposed binomial sampling, where only the proportion of infested leaves was scored rather than the number of spider mites per leaf^9,19. Others estimated damage by TSSM on cotton based on a leaf reddening index scale, which varied from stippling and reddening to extensive reddening of vegetation canopy¹⁹. These methods are arbitrary, anecdotal and biased by individual perceptions of the degree of damage. A more robust and quantitative assessment of the damage caused by TSSM is required to evaluate and separate the treatments with statistical precision.

The ground-based multispectral optical sensor appears to be an improved sampling tool to quantitatively determine the damage inflicted by TSSM and to separate the treatments more precisely than the visual damage scoring system adopted by many researchers. However, researchers have reported that the data intensive hyperspectral remote sensing provided numerous spectral signatures to identify and detect crop stresses and canopy characteristics compared to multispectral remote sensing which is less data intensive with two wave lengths^20,21. Using a hyperspectral spectrometer, Reisig and Godfrey found that the NIR reflectance wavelength ≈850 nm as an informative spectrum in distinguishing arthropod-infested from uninfested cotton²². In this study, we have shown that the multispectral reflectance values (NIR value being ≈770 nm) with just two spectral bands were able to identify and characterize cotton plants infested with varying densities of TSSM. Also, we have reported earlier that the multispectral optical sensor not only effectively separated cotton plants infested with widely varying density categories of TSSM, but also showed that spiromesifen was more effective than abamectin in controlling TSSM in early season cotton in the greenhouse at one-half rate of the lowest label rate²³.

The multispectral optical sensor could be mounted on a mobile research platform and the reflectance values could be obtained from the treated areas of the vegetation canopies without human subjectivity. Acaricidal efficacy data can thus be obtained without much human labor. The NDVI data can be easily uploaded to the computer and analyzed using commercial statistical software. A GPS receiver also can be mounted onto the mobile platform to collect the GPS coordinates in order to generate a damage map of the field. Using multispectral reflectance signatures from the plant canopy, the multispectral optical sensor provides a rapid and cost effective means of identifying and quantifying plant stress. Moreover, a much larger area of the field can be covered in less time with higher spatial resolution of plant canopy compared to conventional field scouting. It is important to remember that the threshold damage level for TSSM on cotton is variable from region to region in the United States. For example, TSSM damage would be higher in an arid environment such as in California compared to the Midsouth region where rainfall and high humidity often prevail²⁴. Therefore, yield loss due to TSSM damage will be variable and so is the damage threshold. However, reports from Mississippi, Arkansas and Tennessee reveal that the threshold level for TSSM on cotton appears to be when 30 to 50% of the plants are infested and populations are actively increasing⁵. Furthermore, field grown plants are exposed to multiple stresses including water stresses and feeding of arthropod herbivores and the interactions between these activities can substantially reduce plant productivity and are likely to influence damage threshold. The TSSM can reduce stomatal conductance, photosynthesis and transpiration rate in cotton²⁵. Plants grown in the greenhouse are influenced by UV light radiation and it does significantly influence stomatal function, photosynthesis and canopy morphology^26,27 and could probably have an additive effect on plant stress. However, TSSM is capable of avoiding UV radiation by having access to habitat protected from solar radiation on the lower surface of the plant canopy^28,29,30, where it resides.

The height of the optical sensor above the target canopy and the orientation of the sensor relative to the target are important factors that affected significantly the reflectance values obtained by the multispectral optical sensor³¹. For instance, when the mobile research platform traverses through a row crop field such as when cotton canopy is open, the sensor is likely to produce different results depending upon the orientation of the sensor, either parallel to or perpendicular to the row. It is also likely that soil and other background materials may greatly influence the sensor readings, especially when the sensor is oriented perpendicular to the row. In order to obtain maximum response from the sensor, the sensor head should be oriented in-line with and directly above the rows. Although orienting the light beam perpendicular to the rows is more likely to pick up background soil reflectance, this may be acceptable, however, when cotton canopy is closed with lush vegetation. In addition, operators should follow manufacturer's recommendations on an operating height range of 81-122 cm and orienting the sensor head in-line with the target to obtain maximum signal response. It is important to charge the sensor battery before use or it should be kept plugged in to avoid troubleshooting. Low battery level is likely to produce erroneous readings.