Dr. Joe Mari J. Maja Profile

Dr. Joe Mari J. Maja

Senior Researcher & Center Director at South Carolina State University

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Proceedings Article | 21 May 2018 Presentation + Paper

Experimental approach to detect water stress in ornamental plants using sUAS-imagery

Ana de Castro, Joe Mari Maja, Jim Owen, James Robbins, Jose Peña

Proceedings Volume 10664, 106640N (2018) https://doi.org/10.1117/12.2304739

KEYWORDS: Sensors, Agriculture, Cameras, Vegetation, Image analysis, Near infrared, Algorithm development, Image segmentation, Roads, Data analysis

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Efficiency in irrigation management is crucial to optimize water use in agriculture. A good irrigation strategy requires accurate and reliable measurements of crop water status that provide dynamic data and timely spatial information. However, this is not feasible with time-consuming manual measurements, which are also prone to cumulative errors due to subjective estimations. Ornamental horticulture crops offer challenges for applying small unmanned aircraft systems (sUAS) technology due to the relatively small area of production and its diversity of plant species. sUAS can operate on demand at low flight height and to carry a wide range of sensors allows capturing the variation of plant traits over time, making it a timely alternative to ground-based data collection in nursery systems. This research evaluated the potential of sUAS-based images to estimate crop water status under three different irrigation regimes. sUAS-imagery of experimental plots was acquired in August 2017 using several multispectral sensors. Container-grown ornamental plants used in the study were Cornus, Hydrangea, Spiraea, Buddleia and Physocarpus. An algorithm based on the object-based image analysis (OBIA) paradigm was applied to retrieve spectral information from each individual plant. Preliminary one-way analysis of variance (ANOVA) identified water stressed and non-stressed plants from data of each study sensor, although spectral separation was higher when information from the sensors was combined. Our results revealed the potential of the sUAS to monitor water status in container-grown ornamental plants, although further analysis is needed to explore vegetation indices and data analysis algorithms.

Proceedings Article | 17 May 2016 Paper

Predicting cotton yield of small field plots in a cotton breeding program using UAV imagery data

Joe Mari Maja, Todd Campbell, Joao Camargo Neto, Philip Astillo

Proceedings Volume 9866, 98660C (2016) https://doi.org/10.1117/12.2228929

KEYWORDS: Unmanned aerial vehicles, Image segmentation, Image processing, Commercial off the shelf technology, Cameras, Gaussian filters, RGB color model, Sensors, Agriculture, Near infrared

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One of the major criteria used for advancing experimental lines in a breeding program is yield performance. Obtaining yield performance data requires machine picking each plot with a cotton picker, modified to weigh individual plots. Harvesting thousands of small field plots requires a great deal of time and resources. The efficiency of cotton breeding could be increased significantly while the cost could be decreased with the availability of accurate methods to predict yield performance.

This work is investigating the feasibility of using an image processing technique using a commercial off-the-shelf (COTS) camera mounted on a small Unmanned Aerial Vehicle (sUAV) to collect normal RGB images in predicting cotton yield on small plot. An orthonormal image was generated from multiple images and used to process multiple, segmented plots. A Gaussian blur was used to eliminate the high frequency component of the images, which corresponds to the cotton pixels, and used image subtraction technique to generate high frequency pixel images. The cotton pixels were then separated using k-means cluster with 5 classes. Based on the current work, the calculated percentage cotton area was computed using the generated high frequency image (cotton pixels) divided by the total area of the plot. Preliminary results showed (five flights, 3 altitudes) that cotton cover on multiple pre-selected 227 sq. m. plots produce an average of 8% which translate to approximately 22.3 kgs. of cotton. The yield prediction equation generated from the test site was then use on a separate validation site and produced a prediction error of less than 10%. In summary, the results indicate that a COTS camera with an appropriate image processing technique can produce results that are comparable to expensive sensors.

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