Dr. Maria Balota Profile

Dr. Maria Balota

at Virginia Polytechnic Institute and State Univ

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Publications (4)

Proceedings Article | 14 May 2019 Paper

Use of unmanned aerial vehicle extracted data to predict health and tiller count in wheat

Alexandre Brice Cazenave, Joseph Oakes, Wade Thomason, Maria Balota

Proceedings Volume 11008, 110080X (2019) https://doi.org/10.1117/12.2518725

KEYWORDS: Unmanned aerial vehicles, Nitrogen, Reflectivity, Remote sensing, RGB color model, Cameras, Tissues, Near infrared, Vegetation, Sensors

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Proceedings Article | 16 May 2017 Presentation + Paper

UAV remote sensing for phenotyping drought tolerance in peanuts

Maria Balota, Joseph Oakes

Proceedings Volume 10218, 102180C (2017) https://doi.org/10.1117/12.2262496

KEYWORDS: Unmanned aerial vehicles, Remote sensing, Tolerancing, Infrared imaging, Agriculture, Yield improvement, Virtual colonoscopy, RGB color model, Near infrared, Infrared radiation, Visualization, Vegetation, Cameras

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Farmers can benefit from growing drought tolerant peanut (Arachis hypogaea L.) cultivars with improved yield when rainfall is sporadic. In the Virginia-Carolina (VC) region, drought is magnified by hot summers and usually occurs in July and Aug when pod and seed growth are intense. At these growth stages, weekly supply of 50 to 75 mm of water is needed to ensure profitability. Irrigation can supplement crop water needs, but only 10% of the peanut farms are irrigated.

In this frame, drought tolerant varieties can be profitable, but breeding for cultivars with improved drought tolerance requires fast yet accurate phenotyping. Our objective was to evaluate the potential of UAV remote sensing technologies for drought tolerance selection in peanut. In this study, we examined the effect of drought on leaf wilting, pod yield, grading characteristics, and crop value of 23 peanut cultivars (Virginia, Runner, and Valencia type). These varieties were arranged in a factorial design, with four replications drought stressed and two replications well-watered. Drought was imposed by covering the drought stressed plots with rainout shelters on July 19; they remained covered until August 29 and only received 38 mm irrigation in mid Aug. The well-watered plots continued to receive rain and supplemental irrigation as needed. During this time, Canopy Temperature Depression (CT) and Normalized Differential Vegetative Index (NDVI) were collected from the ground on all plots at weekly intervals. After the shelters were removed, these measurements were collected daily for approximately 2 weeks. At the same time, Red-Green-Blue (RGB), near-infrared (NIR), and infrared (IR) images taken from an UAV platform were also collected. Vegetation indices derived from the ground and aerial data were compared with leaf wilting, pod yield and crop value. Wilting, which is a common water stress symptom, was best estimated by NDVI and RGB, and least by CT; but CT was best in estimating yield, SMK and crop value in particular when taken on the ground at 15 days water stress imposition. Interestingly, CT predicted well plant wilting even before it occurred, i.e., correlation coefficients were negative and over 0.750 when CT was measured on July 19 and 20 even though wilting was visible only after two weeks. The data, yet preliminary, show promising potential for remote sensing technologies, at the ground and aerial, for peanut variety selection for improved drought tolerance.

Proceedings Article | 16 May 2017 Presentation + Paper

Distinguishing plant population and variety with UAV-derived vegetation indices

Joseph Oakes, Maria Balota

Proceedings Volume 10218, 102180G (2017) https://doi.org/10.1117/12.2262631

KEYWORDS: RGB color model, Near infrared, Vegetation, Unmanned aerial vehicles, Cameras, Agriculture, Remote sensing, Sensing systems, Current controlled current source, Sensors

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Proceedings Article | 17 May 2016 Paper

Exploratory use of a UAV platform for variety selection in peanut

Maria Balota, Joseph Oakes

Proceedings Volume 9866, 98660F (2016) https://doi.org/10.1117/12.2228872

KEYWORDS: Unmanned aerial vehicles, Vegetation, Sensors, Yield improvement, Computed tomography, Unmanned aerial vehicles, Vegetation, RGB color model, Reflectivity, Near infrared, Genetics, Temperature metrology

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Variety choice is the most important production decision farmers make because high yielding varieties can increase profit with no additional production costs. Therefore, yield improvement has been the major objective for peanut (Arachis hypogaea L.) breeding programs worldwide, but the current breeding approach (selecting for yield under optimal production conditions) is slow and inconsistent with the needs derived from population demand and climate change. To improve the rate of genetic gain, breeders have used target physiological traits such as leaf chlorophyll content using SPAD chlorophyll meter, Normalized Difference Vegetation Index (NDVI) from canopy reflectance in visible and near infra-red (NIR) wavelength bands, and canopy temperature (CT) manually measured with infra-red (IR) thermometers at the canopy level; but its use for routine selection was hampered by the time required to walk hundreds of plots. Recent developments in remote sensing-based high throughput phenotyping platforms using unmanned aerial vehicles (UAV) have shown good potential for future breeding advancements. Recently, we initiated a study for the evaluation of suitability of digital imagery, NDVI, and CT taken from an UAV platform for peanut variety differentiation. Peanut is unique for setting its yield underground and resilience to drought and heat, for which yield is difficult to pre-harvest estimate; although the need for early yield estimation within the breeding programs exists. Twenty-six peanut cultivars and breeding lines were grown in replicated plots either optimally or deficiently irrigated under rain exclusion shelters at Suffolk, Virginia. At the beginning maturity growth stage, approximately a month before digging, NDVI and CT were taken with ground-based sensors at the same time with red, blue, green (RGB) images from a Sony camera mounted on an UAV platform. Disease ratings were also taken pre-harvest. Ground and UAV derived vegetation indices were analyzed for disease and yield prediction and further presented in this paper.

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