Allu Ayyappa Reddy

@nitw.ac.in

Research Scholar, Department of Civil Engineering
National Institute of Technology, Warangal

Allu Ayyappa Reddy


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https://researchid.co/ayyappa403

EDUCATION

Research Scholar, National Institute of Technology, Warangal (2021 – till date)
M.Tech in Remote Sensing and GIS, National Institute of Technology, Warangal (2017 – 19)
B.Tech in Civil Engineering, Bapatla Engineering College, Bapatla (2012 – 16)

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering, General Earth and Planetary Sciences, Multidisciplinary, Agronomy and Crop Science
11

Scopus Publications

101

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications
    Ayyappa Reddy Allu, Shashi Mesapam
    Journal of Spatial Science, 2026
    This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.
  • Crop Health Assessment from Predicted AGB and NPK Derived from UAV Spectral Indices and Machine Learning Techniques
    Ayyappa Reddy Allu, Shashi Mesapam
    Agronomy, 2025
    Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m2, N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices.
  • Fusion of Satellite and UAV Imagery for Crop Monitoring
    Ayyappa Reddy Allu, Shashi Mesapam
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2025
    Crop monitoring is crucial for precision agriculture, providing insights for optimizing yield and managing resources effectively. This study explores the fusion of Unmanned Aerial Vehicle (UAV) and Sentinel-2 (S2) satellite imagery for monitoring the crop by analyzing vegetation indices and canopy height information from the temporal dataset. Brovey Transform (BT) and Principal Component Analysis (PCA) fusion techniques are used to fuse the UAV and satellite images, aiming to leverage the high spatial resolution of UAV imagery with the broader spectral range of S2 data. Five key vegetation indices, including NDVI, GNDVI, SAVI, EVI, and LAI, were calculated from UAV, S2, and fused imagery in various temporal dates. Canopy height was derived from UAV data, and statistical analyses, including coefficient of determination (R2), Pearson correlation coefficient, and Root Mean Square Error (RMSE), were performed to assess relationships between canopy height and vegetation indices across the fused images and UAV and S2 images. Results indicate that fused imagery significantly enhances crop health metrics' accuracy and spatial relevance, with high R2 values and strong correlations between vegetation indices of fused images and UAV images, suggesting enhanced predictive power in monitoring crop health. Our findings highlight the advantages of fusing UAV and S2 imagery for comprehensive crop condition assessment, demonstrating that fused images provide a robust tool for monitoring crop vigor and stress levels. This approach offers valuable support for timely, data-driven decisions in crop management practices.
  • Impact of remote sensing data fusion on agriculture applications: A review
    Ayyappa Reddy Allu, Shashi Mesapam
    European Journal of Agronomy, 2025
  • Estimation of Crop Evapotranspiration Under Lower Manair Command Area Using Remote Sensing
    Ayyappa Reddy Allu, Sudhakar Modugu, Shashi Mesapam
    Lecture Notes in Civil Engineering, 2025
  • Impervious Surface Area Prediction Using Landsat Satellite Imagery and Open Source GIS Plugin
    Ayyappa Reddy Allu, Shashi Mesapam
    Lecture Notes in Civil Engineering, 2024
  • Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification
    Ayyappa Reddy Allu, Shashi Mesapam
    Journal of Applied Remote Sensing, 2024
    Crop classification is necessary to extract information about the crop, such as its type, the area in crop that may be used to estimate health, and the yield of the crop. Remote sensing is a technique used to extract earth surface information from satellite and aerial imagery. However, to extract crop parameters accurately requires high spatial and spectral information of the data, which can be achieved through image fusion. We focus on crop classification by selecting the suitable band combination of satellite imagery and unmanned aerial vehicle (UAV) data through image fusion technique. Various fusion techniques are available to produce high spatial and spectral resolution data. Principal component analysis PAN sharpening method is used to fuse Sentinel-2A and UAV data for crop classification, and the best combinations of different bands are assessed based on the image quality metrics and classification accuracy. Random forest classification algorithm is performed to classify the fused images and the classification accuracy is assessed by using the F-score, Kappa coefficient, and overall accuracy. The best accuracy of 93.14% and a kappa value of 0.87 is achieved using random forest classification technique using fused images of red band of UAV imagery with BGRNIR (blue, green, red, and NIR) bands of Sentinel-2A imagery is high compared to other band combinations.
  • Effect of LULC Changes on Land Surface Temperature
    Rajashekar Kummari, Pavan Kumar Reddy Allu, Shashi Mesapam, Ayyappa Reddy Allu, Bhargavi Vinakallu, Bhanu Prakash Ankam
    Lecture Notes in Civil Engineering, 2024
  • IMPACT OF UAV AND SENTINEL-2A IMAGERY FUSION ON VEGETATION INDICES PERFORMANCE
    A. Ayyappa Reddy, M. Shashi
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2023
    Image fusion techniques can improve the quality of remote sensing images by combining high spatial resolution images with low spectral resolution images. This enhancement of the images may impact the performance of various vegetation indices (VI’s). This study investigates the impact of image fusion on the quality of vegetation indices by fusing UAV (Unmanned Aerial Vehicle) bands with Sentinel-2A images using Principal Component Analysis (PCA) and Brovery Transform (BT) fusion techniques.The fused images were used to calculate the Normalized Difference Vegetation Index, Normalized Difference Red Edge, Green Red Vegetation Index, and Normalized Difference Water Index. To assess the performance of the fused images, several image quality assessment metrics were used, including Root Mean Square Error (RMSE), Entropy, etc...The results showed that image fusion techniques can improve the quality of images which is important to assess crop health. The PCA image fusion technique showed higher quality than the BT technique. The PCA fused images had lower RMSE, ERGAS, and Entropy Difference and higher UIQI, CC, and SSIM values than the original images. Moreover, the fused images produced higher VIs values than the Sentinel-2A images.Finally, scatter plots were created to compare the correlation between the VIs calculated from the original and fused images. The results showed a strong correlation between the VIs calculated from the Sentinel-2A and fused images, indicating that the fused images can accurately estimate vegetation health parameters. Overall, this study demonstrates the potential of image fusion techniques to improve the quality of VI’s for monitoring vegetation health.
  • Algal Bloom Detection Using UAV Imagery: A Case Study on Waddepally Lake, Warangal
    Allu Ayyappa Reddy, M. Shashi, Kumarapu Kumar
    Lecture Notes in Civil Engineering, 2023
  • REAL-TIME OPTIMIZATION OF TRAFFIC SIGNALING TIME USING CNN
    Suranaree Journal of Science and Technology, 2021

RECENT SCHOLAR PUBLICATIONS

  • Assessment of Temporal Variations in Crop Growth Dynamics Using UAV Imagery
    AR Allu, S Mesapam
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2026
    2026
  • Crop Health Assessment from Predicted AGB and NPK Derived from UAV Spectral Indices and Machine Learning Techniques
    AR Allu, S Mesapam
    Agronomy 15 (9), 2059 , 2025
    2025
    Citations: 4
  • Fusion of Satellite and UAV Imagery for Crop Monitoring
    AR Allu, S Mesapam
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
    Citations: 13
  • Impact of remote sensing data fusion on agriculture applications: A review
    AR Allu, S Mesapam
    European Journal of Agronomy 164, 127478 , 2025
    2025
    Citations: 41
  • Estimation of Crop Evapotranspiration Under Lower Manair Command Area Using Remote Sensing
    AR Allu, S Modugu, S Mesapam
    Climate Change Impact on Water Resources (HYDRO 2023) 561, 319-337 , 2025
    2025
  • Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications
    AR Allu, S Mesapam
    Journal of Spatial Science , 2024
    2024
    Citations: 5
  • Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification
    AR Allu, S Mesapam
    Journal of Applied Remote Sensing 18 (1), 016511 , 2024
    2024
    Citations: 14
  • Impervious Surface Area Prediction Using Landsat Satellite Imagery and Open Source GIS Plugin
    AR Allu, M Shashi
    Developments and Applications of Geomatics 1, 311-325 , 2024
    2024
  • Impact of Uav and SENTINEL-2A Imagery Fusion on Vegetation Indices Performance
    A Ayyappa Reddy, M Shashi
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2023
    2023
    Citations: 5
  • Algal Bloom Detection Using UAV Imagery: A Case Study on Waddepally Lake, Warangal
    A Ayyappa Reddy, M Shashi, K Kumar
    Proceedings of UASG 2021: Wings 4 Sustainability 304, 423-434 , 2023
    2023
    Citations: 1
  • Effect of LULC changes on land surface temperature
    R Kummari, PKR Allu, S Mesapam, AR Allu, B Vinakallu, BP Ankam
    Developments and Applications of Geomatics: Proceedings of DEVA 2022 450 … , 2022
    2022
    Citations: 2
  • Real-Time Optimization of Traffic Signaling Time Using CNN
    A Ayyappa Reddy, M Shashi
    Suranaree Journal of Science and Technology 28 (6), 010085(1-8) , 2021
    2021
    Citations: 2
  • Compressive Strength of PPC Based Quaternary Blended Concrete
    G Swamy Yadav, RA Ayyappa, M Guruprasad, G Hari Prasad, S Vyshnavi, ...
    IOP Conference Series: Materials Science and Engineering 925 (1), 012007 , 2020
    2020
    Citations: 1
  • Partial replacement of cement and coarse aggregate by egg shell powder and coconut shells
    DSS Ayyappa R A, Sandeep Reddy B, G. Swamy Yadav
    INTERNATIONAL JOURNAL 9 (4), 1242-1246 , 2020
    2020
    Citations: 13

MOST CITED SCHOLAR PUBLICATIONS

  • Impact of remote sensing data fusion on agriculture applications: A review
    AR Allu, S Mesapam
    European Journal of Agronomy 164, 127478 , 2025
    2025
    Citations: 41
  • Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification
    AR Allu, S Mesapam
    Journal of Applied Remote Sensing 18 (1), 016511 , 2024
    2024
    Citations: 14
  • Fusion of Satellite and UAV Imagery for Crop Monitoring
    AR Allu, S Mesapam
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
    Citations: 13
  • Partial replacement of cement and coarse aggregate by egg shell powder and coconut shells
    DSS Ayyappa R A, Sandeep Reddy B, G. Swamy Yadav
    INTERNATIONAL JOURNAL 9 (4), 1242-1246 , 2020
    2020
    Citations: 13
  • Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications
    AR Allu, S Mesapam
    Journal of Spatial Science , 2024
    2024
    Citations: 5
  • Impact of Uav and SENTINEL-2A Imagery Fusion on Vegetation Indices Performance
    A Ayyappa Reddy, M Shashi
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2023
    2023
    Citations: 5
  • Crop Health Assessment from Predicted AGB and NPK Derived from UAV Spectral Indices and Machine Learning Techniques
    AR Allu, S Mesapam
    Agronomy 15 (9), 2059 , 2025
    2025
    Citations: 4
  • Effect of LULC changes on land surface temperature
    R Kummari, PKR Allu, S Mesapam, AR Allu, B Vinakallu, BP Ankam
    Developments and Applications of Geomatics: Proceedings of DEVA 2022 450 … , 2022
    2022
    Citations: 2
  • Real-Time Optimization of Traffic Signaling Time Using CNN
    A Ayyappa Reddy, M Shashi
    Suranaree Journal of Science and Technology 28 (6), 010085(1-8) , 2021
    2021
    Citations: 2
  • Algal Bloom Detection Using UAV Imagery: A Case Study on Waddepally Lake, Warangal
    A Ayyappa Reddy, M Shashi, K Kumar
    Proceedings of UASG 2021: Wings 4 Sustainability 304, 423-434 , 2023
    2023
    Citations: 1
  • Compressive Strength of PPC Based Quaternary Blended Concrete
    G Swamy Yadav, RA Ayyappa, M Guruprasad, G Hari Prasad, S Vyshnavi, ...
    IOP Conference Series: Materials Science and Engineering 925 (1), 012007 , 2020
    2020
    Citations: 1
  • Assessment of Temporal Variations in Crop Growth Dynamics Using UAV Imagery
    AR Allu, S Mesapam
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2026
    2026
  • Estimation of Crop Evapotranspiration Under Lower Manair Command Area Using Remote Sensing
    AR Allu, S Modugu, S Mesapam
    Climate Change Impact on Water Resources (HYDRO 2023) 561, 319-337 , 2025
    2025
  • Impervious Surface Area Prediction Using Landsat Satellite Imagery and Open Source GIS Plugin
    AR Allu, M Shashi
    Developments and Applications of Geomatics 1, 311-325 , 2024
    2024

GRANT DETAILS

Received a TIF travel grant by the ISPRS Foundation
Received International Travel Support from ANRF
Received CSIR Travel Grant from CSIR HRDG
Received a travel grant by the ASCE CISSC 2025 organizing committee