About

Juan Enciso is a professor in the Department of Biological and Agricultural Engineering at Texas A&M University. He holds a B.S. in Irrigation Engineering from Universidad Autonoma Agraria “Antonio Narro” in Saltillo, Coahuila, Mexico, obtained in 1984, a M.S. in Water Management from Instituto Tecnologico de Estudios Superiores de Monterrey in Monterrey, Nuevo Leon, Mexico, earned in 1986, and a Ph.D. in Agricultural Engineering from the University of Nebraska-Lincoln in 1992. His areas of expertise include deficit irrigation, salinity management, irrigation of bioenergy crops, infiltration and overland flow, irrigation of vegetable crops, hydrologic impacts of land and use practices in agricultural regions, modeling and optimization research for managing irrigation, water measurement of irrigation water, screening of drought tolerant and salinity tolerant crops, and water use efficiency of bioenergy crops. He is a member of Texas A&M AgriLife, which encompasses Texas A&M AgriLife Extension Service, Texas A&M AgriLife Research, Texas A&M Forest Service, Texas A&M Veterinary Medical Diagnostic Lab, and the College of Agriculture & Life Sciences.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Biology
• Geography
• Remote sensing
• Agronomy
• Statistics
• Medicine
• Computer vision
• Cartography
• Horticulture
• Algorithm
• Ecology
• Forestry
• Veterinary medicine

Selected publications

• Combination of remote sensing with crop modeling using Bayesian inferences to predict irrigated cotton yield

  Agricultural Water Management · 2025-07-17 · 3 citations

  articleOpen accessSenior author

  The primary goal of this study was to create a Bayesian framework that would incorporate remote sensing data to automatically calibrate the AquaCrop model for simulating cotton responses to irrigation strategies in the northern border of the United States Cotton Belt, which faces a lack of observational data. Multiple regression models (linear and non-linear) were fitted to establish a correlation between cotton canopy cover (CC) values and aerial vegetation indices (EVI, EVI2, MACARI, NDRE, NDVI, NDSVI, OSAVI, and VARI) obtained from sUAS multispectral imagery for 2021 and 2022 growing seasons. The highest correlation was found between RGB-Based VARI index and cotton CC by fitting the linear model (R 2 = 0.83 and RMSE = 0.12), which contradicted the results of other studies that emphasized the importance of using red-edge and near-infrared for monitoring crop canopy cover. A considerably less accurate correlation was detected for fitting the polynomial model (0.4 <RMSE<4.2). Furthermore, the MCARI index was found unsuitable for cotton monitoring under water stress conditions. Afterward, the Bayesian theorem-based Generalized Likelihood Uncertainty Estimation (GLUE) algorithm was linked to AquaCrop in the R environment to calibrate the model based on the remotely sensed CCs by seeking posterior distributions of the parameters through the Monte Carlo approach. Then, the model was validated for its key outputs, including cotton biomass, yield, and soil water content. The simulated CC results showed the model's automatic calibration success. The best performances of the model were found for simulating cotton biomass under 70 % and 80 % deficit irrigation conditions Pe = 0.88 % and −0.38 %) in 2022 and full irrigation conditions in 2021 Pe = 3.19 %); however, the biomass simulations were satisfactorily under all irrigation conditions. The outstanding performance of the AquaCrop was confirmed for reproducing cotton yield values regardless of irrigation conditions. The accurate retrieval of soil water dynamics by the model can introduce the framework created in this study as a robust tool to derive soil water content at cotton rootzone by having access to aerial RGB images. Overall, the findings of this study revealed that by supplying the introduced framework with one seasonal remote sensing data, the AquaCrop could successfully be turned into a decision support system for exploring irrigation scheduling strategies for producers of cotton in Kansas. • The simple RGB imagery could accurately monitor cotton response to irrigation. • The GLUE algorithm was successfully linked to AquaCrop model. • For the first time the AquaCrop was calibrated for cotton responses to irrigation in Kansas. • Remotely sensed cotton canopy cover data are adequate for AquaCrop calibration. • The framework created can accurately connect RGB images to soil moisture content.

  PublisherDOI

• Estimating sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP)

  Computers and Electronics in Agriculture · 2025-06-14 · 5 citations

  articleOpen access

  • Millable to plant height ratio played an important role in predicting millable stalk height. • Canopy height model-derived canopy cover was a promising feature to predict stalk number. • Unmanned vehicle system-based yield components performed equally to the traditional assessment. • Untested crop/environment had a higher negative influence on the model accuracy than untested cultivars. • Prediction the performance of the cultivars in untested environments appeared to be most challenging. Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R 2 = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R 2 = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R 2 = 0.69, RMSE = 10,975 stalks ha −1 ). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally, SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly, the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.

  PublisherDOI

• AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

  Remote Sensing · 2024-07-27 · 3 citations

  articleOpen accessSenior author

  To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.

  PublisherDOI

• Estimating Sugarcane Yield and its Components Using Unmanned Aerial Systems (Uas)- Based High Throughput Phenotyping (Htp)

  SSRN Electronic Journal · 2024-01-01 · 3 citations

  preprintOpen accessSenior author
  PublisherDOI

• Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning

  Sensors · 2024-11-29 · 2 citations

  articleOpen accessSenior authorCorresponding

  Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus.

  PublisherDOI

• Assessing Drought Stress of Sugarcane Cultivars Using Unmanned Vehicle System (UAS)-Based Vegetation Indices and Physiological Parameters

  Remote Sensing · 2024-04-18 · 11 citations

  articleOpen access

  Sugarcane breeding for drought tolerance is a sustainable strategy to cope with drought. In addition to biotechnology, high-throughput phenotyping has become an emerging tool for plant breeders. The objectives of the present study were to (1) identify drought-tolerant cultivars using vegetation indices (VIs), compared to the traditional method and (2) assess the accuracy of VIs-based prediction model estimating stomatal conductance (Gs) and chlorophyll content (Chl). A field trial was arranged in a randomized complete block design, consisting of seven cultivars of sugarcane. At the tillering and elongation stages, irrigation was withheld, and then furrow irrigation was applied to relieve sugarcane from stress. The physiological assessment measuring Gs and Chl using a handheld device and VIs were recorded under stress and recovery periods. The results showed that the same cultivars were identified as drought-tolerant cultivars when VIs and traditional methods were used for identification. Likewise, the results derived from genotype by trait biplot and heatmap were comparable, in which TCP93-4245 and CP72-1210 cultivars were classified as tolerant cultivars, while sensitive cultivars were CP06-2400 and CP89-2143 for both physiological parameters and VIs-based identification. In the prediction model, the random forest outperformed linear models in predicting the performance of cultivars in untested crops/environments for both Gs and Chl. In contrast, it underperformed linear models in the tested crops/environments. The identification of tolerant cultivars through prediction models revealed that at least two out of three cultivars had consistent rankings in both measured and predicted outcomes for both traits. This study shows the possibility of using UAS mounted with sensors to assist plant breeders in their decision-making.

  PublisherOA PDFDOI

• Estimating Tree-level Yield of Citrus fruit using Multi-temporal using UAS datasets

  2023-10-30

  preprintOpen access

  In the quest to enhance citrus yield estimation, this study leverages Multi-Temporal Unoccupied Aerial Systems (UAS) datasets to develop a refined tree-level yield estimation model. Conducted within a 2.22-acre orchard in Weslaco, South Texas, the research integrates data from 56 citrus trees, utilizing RGB and Multispectral UAS imagery collected over critical growth months from June to December (excluding July and August) 2022. The methodology encompasses the collection of UAS data processing using some custom-developed algorithms and automated workflows and the novel application of machine learning algorithms-namely, multiple linear regression, gradient boosting regression, and random forest regression. The study innovatively extracts 11 key phenotypic features, combining tree canopy structural and spectral information to estimate yield with increased accuracy. A significant advancement is proposed in the form of an improved individual tree boundary delineation method. This method addresses the inaccuracies of previous solid shape-based approaches, contributing to more precise feature calculation and improved model performance. Our experimental results indicate the single month whose data is particularly predictive, with the Random Forest model demonstrating robustness and consistency across temporal datasets. The multi-temporal approach confirms that comprehensive data integration yields superior estimation models. This ongoing research promises to bridge the yield estimation gap and set a new standard in precision agriculture methodologies.

  PublisherOA PDFDOI

• Plastic Contaminant Detection in Aerial Imagery of Cotton Fields Using Deep Learning

  Agriculture · 2023-07-09 · 6 citations

  articleOpen access

  Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p &lt; 0.001) and their height on cotton plants (p &lt; 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.

  PublisherOA PDFDOI

• Abstract 2194: Performance of a 24-hour turnaround, guideline-complete NSCLC assay, optimized for sparse samples

  Cancer Research · 2023-04-04

  article

  Abstract Non-Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer with 236,740 new cases diagnosed in 2022 in the United States. In this study, we demonstrate the performance of a digital PCR (dPCR) based assay that reports 15 (NCCN) biomarkers using only 1 slide of FFPE tissue. ChromaCode’s High Definition PCR (HDPCR™) NSCLC Tumor Profiling Panel (Research Use Only) detects guideline biomarkers including variants for EGFR, BRAF, KRAS, ERBB2, ALK, ROS1, RET, MET, and NTRK1-3 with a 24 hour turnaround time and a simple PCR workflow. A total of 34 NSCLC FFPE specimens were evaluated using the HDPCR NSCLC Panel. DNA and RNA were extracted from a single 10 µm section of specimen, with a sister section previously characterized with the Oncomine Precision Assay GX. The DNA/RNA eluates (20ng DNA per well, 20ng RNA) were run with the HDPCR NSCLC Panel on the Qiagen QIAcuity dPCR instrument. Data analysis was conducted using ChromaCloud™, which includes a proprietary algorithm that can detect and quantify 6 targets (5 targets and 1 internal control) for each of the 3 wells. DNA samples discordant with the comparator assay were evaluated using the Archer DNA VariantPlex panel, while RNA discordant samples were analyzed using the Archer RNA fusion plex panel. Of the 34 specimens evaluated, 16 SNV, 13 Insertion/Deletion, and 7 fusion positive specimens were identified using NGS. On the dPCR assay, the results show a high-level of agreement, after discordant resolution, of 95.8% across the panel. The minor allele fraction (MAF), also calculated using ChromaCode Cloud, ranged from 5% to 90%. With 20 ng input (Qubit), the average copy number of amplifiable DNA ranged from 791 copies (~2.3 ng) to 6583 copies (~19.9 ng) with a mean of 2410 copies (~7.43 ng). Here we demonstrate that the HDPCR NSCLC Tumor Profiling Panel provides comparable performance to validated NGS assays with a turnaround that enables adherence to guideline-recommended treatment, and a low sample requirement that maximizes the number of patients for molecular testing. The standard protocols used by the assay and the use of a cloud based analysis pipeline enables easy deployment of this assay in any lab. Citation Format: Kerri Cabrera, Jeff Gole, Bryan Leatham, Isabel Regoli, Tiffany Martinez, Andrew Richards, Leah Herdt, Juan Enciso, Paige Berroteran, Heather Carolan, Brad Brown. Performance of a 24-hour turnaround, guideline-complete NSCLC assay, optimized for sparse samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2194.

  PublisherDOI

• The use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield

  Journal of Agriculture and Food Research · 2023-01-06 · 14 citations

  articleOpen access

  A sensor mounted on an unmanned aerial system (UAS) may enable breeding programs’ selection efficiency. The objectives of this study were to investigate the use of UAS with red, green, and blue (RGB) camera to assess sugarcane yield and to investigate the direct and indirect influences of canopy features on cane yield of sugarcane. A trial was conducted from 2019 to 2021 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, and arranged in a complete randomized block design, consisting of 7 genotypes with 4 replications. Seven UAS image acquisitions were performed at the plant cane stage using an RGB sensor. At the first ratoon stage, five flights were conducted using RGB and multispectral sensors. Ground measurements were obtained, including pol, tons of sugar per hectare (TSH), and tons of cane per hectare (TCH). The results showed that the correlation of canopy features with pol at the elongation phase was poor, compared to those acquired at the maturity phase. Likewise, the most significant correlations between canopy features with TSH and TCH were found in the late elongation to maturity phase. In addition, a good agreement of the validation dataset between the observed and predicted pol (R2 0.58, RMSE 0.77%), TSH (R2 0.68, RMSE 2.24 Mg ha−1), and TCH (R2 0.66, RMSE 13.64 Mg ha−1) was observed. The canopy height model (CHM) and Normalized Difference Vegetation Index (NDVI) had the highest direct effect on TCH. Therefore, assessing sugarcane yield using UAS-based imagery looks promising for High Throughput Phenotyping (HTP) in sugarcane breeding programs.

  PublisherDOI

Frequent coauthors

• Bruce J. Lesikar

  Architecture Technology Corporation (United States)

  16 shared

• Robert G. Hardin

  15 shared

• Pappu Kumar Yadav

  15 shared

• Sorin Popescu

  Texas A&M University

  15 shared

• Ulisses Braga-Neto

  15 shared

• Stephen W. Searcy

  Texas A&M University

  15 shared

• J. Alex Thomasson

  Mississippi State University

  14 shared

• Karem Meza

  Utah State University

  11 shared

Education

• B.S., Irrigation Engineering

  Universidad Autonoma Agraria “Antonio Narro”

  1984

• M.S., Water Management

  Instituto Tecnologico de Estudios Superiores de Monterrey

  1986

• Ph.D., Agricultural Engineering

  University of Nebraska-Lincoln

  1992