Remote Sensing, Image Processing, Computer Vision, Machine Learning, Multimedia System
34
Scopus Publications
227
Scholar Citations
8
Scholar h-index
8
Scholar i10-index
Scopus Publications
Lignans Associated Differences in Salt Stress Responses of Flax (Linum usitatissimum L.) Genotypes In Vitro Moumita Roy Chowdhury, Katarína Ražná, Jindra Valentová, Emil Švajdlenka, Eva Ivanišová, Anirban Jyoti Debnath, Jit Mukherjee, Veronika Štefúnová, Mizgin Mehmet, Paračková Patrícia, Marián Miko Cells, 2026 The objective of this study was to investigate the association between lignan content and stress responses in flax genotypes with contrasting lignan levels. For this purpose, two flax (Linum usitatissimum L.) genotypes, Agram and CDC Bethune, were selected based on their differing lignan profiles. We quantified secoisolariciresinol diglucoside, pinoresinol, pinoresinol diglucoside, matairesinol, and lariciresinol in both control and salt-stressed plants. In parallel, antioxidant activity, flavonoid, polyphenols, and phenolic acid content were determined to assess the overall antioxidant potential and phenolic response under saline conditions. The Agram genotype appears to activate defense mechanisms that enhance antioxidant capacity, which is largely mediated by polyphenolic compounds and distinct patterns of microRNA regulation. By contrast, the CDC Bethune genotype primarily responds to salinity stress by inducing lignan biosynthesis. Differential lignan modulation, contrasting antioxidants and miRNA profiles, shows substantial intergenotypic differences in how flax activates distinct defense pathways.
Leveraging Sentinel-2 Data and Machine Learning for Drought Detection in India: The Process of Ground Truth Construction and a Case Study Shubham Subhankar Sharma, Jit Mukherjee, Fabio Dell’Acqua Remote Sensing, 2025 Droughts significantly impact agriculture, water resources, and ecosystems. Their timely detection is essential for implementing effective mitigation strategies. This study explores the use of multispectral Sentinel-2 remote sensing indices and machine learning techniques to detect drought conditions in three distinct regions of India, such as Jodhpur, Amravati, and Thanjavur, during the Rabi season (October–April). Twelve remote sensing indices were studied to assess different aspects of vegetation health, soil moisture, and water stress, and their possible joint use and influence as indicators of regional drought events. Reference data used to define drought conditions in each region were primarily sourced from official government drought declarations and regional and national news publications, which provide seasonal maps of drought conditions across the country. Based on this information, a district vs. year (3 × 10) ground truth is created, indicating the presence or absence of drought (Drought/No Drought) for each region across the ten-year period. Using this ground truth table, we extended the remote sensing dataset by adding a binary drought label for each observation: 1 for “Drought” and 0 for “No Drought”. The dataset is organized by year (2016–2025) in a two-dimensional format, with indices as columns and observations as rows. Each observation represents a single measurement of the remote sensing indices. This enriched dataset serves as the foundation for training and evaluating machine learning models aimed at classifying drought conditions based on spectral information. The resultant remote sensing dataset was used to predict drought events through various machine learning models, including Random Forest, XGBoost, Bagging Classifier, and Gradient Boosting. Among the models, XGBoost achieved the highest accuracy (84.80%), followed closely by the Bagging Classifier (83.98%) and Random Forest (82.98%). In terms of precision, Bagging Classifier and Random Forest performed comparably (82.31% and 81.45%, respectively), while XGBoost achieved a precision of 81.28%. We applied a seasonal majority voting strategy, assigning a final drought label for each region and Rabi season based on the majority of predicted monthly labels. Using this method, XGBoost and Bagging Classifier achieved 96.67% accuracy, precision, and recall, while Random Forest and Gradient Boosting reached 90% and 83.33%, respectively, across all metrics. Shapley Additive Explanation (SHAP) analysis revealed that Normalized Multi-band Drought Index (NMDI) and Day of Season (DOS) consistently emerged as the most influential features in determining model predictions. This finding is supported by the Borda Count and Weighted Sum analysis, which ranked NMDI, and DOS as the top feature across all models. Additionally, Red-edge Chlorophyll Index (RECI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI) were identified as important features contributing to model performance. These features help reveal the underlying spatiotemporal dynamics of drought indicators, offering interpretable insights into model decisions. To evaluate the impact of feature selection, we further conducted a feature ablation study. We trained each model using different combinations of top features: Top 1, Top 2, Top 3, Top 4, and Top 5. The performance of each model was assessed based on accuracy, precision, and recall. XGBoost demonstrated the best overall performance, especially when using the Top 5 features.
Chitosan as an Elicitor in Plant Tissue Cultures: Methodological Challenges Moumita Roy Chowdhury, Mizgin Mehmet, Jit Mukherjee, Anirban Jyoti Debnath, Katarína Ražná Molecules, 2025 Chitosan (CTS) is a biodegradable and biocompatible biopolymer derived from chitin. Thanks to its diverse biological activities and environmentally friendly nature, it has emerged as a promising agent in plant tissue culture. Recent studies have highlighted its role as a natural elicitor that can enhance plant growth, seed germination, and the biosynthesis of secondary metabolites in vitro. In plant tissue culture, it acts as a biotic elicitor, mimicking a pathogen attack and activating the pathogenesis-related proteins to induce secondary metabolite production. In vitro tissue culture is a scientifically meaningful and cost-effective approach to testing the elicitation mechanisms of various abiotic elicitors, including CTS. However, the methodology of CTS elicitation in plant tissue cultures is not straightforward or uniform due to the differences in the CTS origin, molecular weight, and degree of deacetylation, all of which directly affect solubility. This review summarizes the methodological approaches to the use of CTS in plant tissue culture elicitation and highlights specific features of these procedures.
EchoCNN-Denoiser: a reservoir computing inspired deep learning model for enhanced synthetic aperture radar image despeckling Swarna Aishwarya Twinkle, Supreeti Kamilya, Jit Mukherjee Journal of Applied Remote Sensing, 2025 The occurrence of speckle noise in synthetic aperture radar (SAR) images significantly impacts the accurate extraction of essential information for remote sensing applications. To address this issue, a denoising model, named EchoCNN-Denoiser, is proposed that utilizes the combined strengths of convolutional neural networks (CNN) and reservoir computing (RC) to effectively minimize speckle noise and improve the quality of SAR images. RC offers a method that employs the temporal processing capabilities of recurrent neural network (RNN) without the complexity of training them. We utilized CNN for spatial feature extraction from SAR images, and echo state network (ESN), a concept of RC, is utilized for capturing temporal dependencies, resulting in a robust framework for SAR image denoising. CNN extracts hierarchical features using convolution and pooling operations and the ESN transforms these features into a higher-dimensional space through a sparsely connected reservoir. To optimize performance, the reservoir-to-output weights are trained with parameters such as spectral radius, input scaling, and sparsity. Through extensive experiments on real and virtual SAR datasets, the proposed technique demonstrates superior performance in noise reduction compared with existing methods. The model is also validated using multiple evaluation techniques. Initially, our model is compared against traditional filters and other deep learning denoising methods to evaluate its relative performance which is tested using image quality metrics such as peak signal-to-noise ratio, structural similarity index measure, equivalent number of looks, perception-based image quality evaluator and blind/referenceless image spatial quality evaluator, and perceptual index, achieving scores of 30.01, 0.90, 9.48, 27.22, 41.65, and 34.44, respectively. Following that, layer-wise relevance propagation (LRP) is applied to gain a deeper understanding of the model predictions. The heatmaps obtained from LRP visualization confirm that EchoCNN-Denoiser effectively preserves essential image structures while reducing noise. Further, a paired t-test is conducted to statistically assess the effectiveness of the model. Finally, an ablation study is performed to evaluate the contribution of each component to the overall performance of the model. These validations demonstrate the effectiveness of EchoCNN-Denoiser in producing high-quality, despeckled SAR images. As noise reduction is the primary step of SAR image processing, the proposed technique has several applications in remote sensing, by significantly enhancing the SAR image quality.
Cross-Referencing Youtube Comments and Multi-spectral Images in Flood-Affected Areas: A Case Study of India and Bangladesh Purushottam Kumar, Jit Mukherjee, Richa Singh International Geoscience and Remote Sensing Symposium IGARSS, 2025 Floods are one of the natural disasters that have a direct impact on both human lives and the environment. In this study, the relationship between different public opinions during floods and concurrently noticeable environmental changes is examined. It analyzes emotional responses, which can be drawn from social media, such as YouTube comments. Further, the work incorporates the use of spectral indexes from multispectral data to assess environmental changes and associate them with sentiment analysis. In this paper, the remote sensing indices NDVI, NDWI, and Urban Index are combined with YouTube data for flood evaluation. In Bengaluru, India, the NDWI went up by 127%, whereas NDVI reduced by 110%, and the Urban Index fell by 65%. Sylhet, Bangladesh recorded an increase of 131% for NDWI, a decrease of 133% for NDVI, and a decline of 77.89% for the Urban Index. Correlations between YouTube comments and the metrics were quite strong, up to 0.84 on Pearson values, and Maximum Information Coefficient (MIC) could reach 0.88. The findings highlight the integral relationship between social media and geospatial tools in catastrophe monitoring and response.
INFLUENCE OF VEGETATION FEATURES ON CORN YIELDS ESTIMATION USING DIFFERENT MACHINE LEARNING TECHNIQUES: A CASE STUDY Jit Mukherjee, Fabio Dell’Acqua 2024 IEEE India Geoscience and Remote Sensing Symposium Ingarss 2024, 2024 Yield prediction is a critical aspect of agricultural management. Recent advancements in remote sensing technologies enabled use of satellite imagery to estimate crop yields, with vegetation features being a key element. The impact of each feature, however, varies with different crops, and different vegetation features may be influential for different models for a single crop such as corn. Hence, a study would be beneficial to quantify influence of vegetation features over different models. The paper provides a case study over a publicly accessible dataset of corn yield to understand the influence of ten vegetation features in different regression models. It has been observed that the vegetation features sensitive to chlorophyll content and inclusion of Green channel provide significant contributions to corn yield estimation. The proposed technique has also been applied on soybean and wheat yield prediction to study the influence of vegetation features.
VIMARSHAK - A web based subjective image evaluation system Gazal Garg, Prasenjit Mondal, Shashaank M. Aswatha, Jit Mukherjee, Tapas Maji, Jayanta Mukherjee Proceedings 2014 5th International Conference on Signal and Image Processing Icsip 2014, 2014
RECENT SCHOLAR PUBLICATIONS
Lignans Associated Differences in Salt Stress Responses of Flax ( Linum usitatissimum L.) Genotypes In Vitro M Roy Chowdhury, K Ražná, J Valentová, E Švajdlenka, E Ivanišová, ... Cells 15 (9), 796 , 2026 2026
GridLife: A Game of Life Inspired Non-parametric Grid-Based Linear-Scalable Density Evolution Framework for Clustering J Mukherjee Asian Symposium on Cellular Automata Technology, 21-32 , 2026 2026
Leveraging sentinel-2 data and machine learning for drought detection in India: The process of ground truth construction and a case study SS Sharma, J Mukherjee, F Dell’Acqua Remote Sensing 17 (18), 3159 , 2025 2025 Citations: 5
Chitosan as an elicitor in plant tissue cultures: Methodological challenges M Roy Chowdhury, M Mehmet, J Mukherjee, AJ Debnath, K Ražná Molecules 30 (17), 3476 , 2025 2025 Citations: 11
Discrimination of river sandbanks for sand mining in high-mineral regions using multispectral images J Mukherjee Discover Geoscience 3 (1), 100 , 2025 2025
Cross-Referencing Youtube Comments and Multi-spectral Images in Flood-Affected Areas: A Case Study of India and Bangladesh P Kumar, J Mukherjee, R Singh IGARSS 2025-2025 IEEE International Geoscience and Remote Sensing Symposium … , 2025 2025
Cultivating Insights: Unsupervised Mapping of Inter-row Management inVineyards Using Bezier Curve Properties on Sentinel-2 Time Series F Dell'Acqua, J Mukherjee EGU General Assembly Conference Abstracts, EGU25-12158 , 2025 2025
EchoCNN-Denoiser: a reservoir computing inspired deep learning model for enhanced synthetic aperture radar image despeckling SA Twinkle, S Kamilya, J Mukherjee Journal of Applied Remote Sensing 19 (2), 026501-026501 , 2025 2025 Citations: 4
Despeckling Images Using Elementary Cellular Automata S Aishwarya Twinkle, S Kamilya, J Mukherjee Asian Symposium on Cellular Automata Technology, 191-202 , 2025 2025
An Elementary Cellular Automata Based Two-Class Data Imbalance Problem: Initial Study and Observations N Kumari, S Kanungo, J Mukherjee Asian Symposium on Cellular Automata Technology, 57-68 , 2025 2025 Citations: 1
Leveraging Sentinel-2 Data and Machine Learning for Drought Detection in India: A Case Study SS Sharma, J Mukherjee, F Dell'Acqua 2025
Edge Preserving Multiplicative Noise Removal of SAR Images Through Convolutional Neural Network and Anisotropic Diffusion SA Twinkle, S Kamilya, J Mukherjee 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1-4 , 2024 2024 Citations: 1
Tillage Monitoring: Determining the Optimal Number of Features in Multi-Spectral Images: a Case Study in the Indo-Gangetic Plains S Rajabzadeh, J Mukherjee, F Dell’Acqua 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1-4 , 2024 2024
Influence of Vegetation Features on Corn Yields Estimation Using Different Machine Learning Techniques: A Case Study J Mukherjee, F Dell’Acqua 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1-4 , 2024 2024 Citations: 1
Connecting the dots: Isolated trails of detected narrow rivers in multispectral images J Mukherjee, JB Courbot International Conference on Pattern Recognition, 332-345 , 2024 2024 Citations: 1
Identifying the Changes of Mine Water Bodies from Landsat 8 OLI Images in Automated Manner: A Case Study in Jharia, India J Mukherjee Water Informatics: Challenges and Solutions Using State of Art Technologies … , 2024 2024
Deciphering miRNA‐lncRNA‐mRNA interaction through experimental validation of miRNAs, lncRNAs, and miRNA targets on mRNAs in Cajanus cajan MR Chowdhury, C Chatterjee, D Ghosh, J Mukherjee, S Shaw, J Basak Plant Biology , 2024 2024 Citations: 11
A Study of Quantifying the Deviation of Remotely Sensed Objects from Multi-spectral Images P Tewary, J Mukherjee International Conference on Pattern Recognition and Machine Intelligence … , 2023 2023
Are Raw Coals Transported on This Road? A Brief Discussion Using Landsat 8 Oli Images J Mukherjee IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium … , 2023 2023
Identifying rivers with varying width through NDWI from Landsat 8 images J Mukherjee IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium … , 2023 2023 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
A novel index to detect opencast coal mine areas from Landsat 8 OLI/TIRS J Mukherjee, J Mukherjee, D Chakravarty, S Aikat IEEE Journal of Selected Topics in Applied Earth Observations and Remote … , 2019 2019 Citations: 48
A survey on image retrieval performance of different bag of visual words indexing techniques J Mukherjee, J Mukhopadhyay, P Mitra Proceedings of the 2014 IEEE Students' Technology Symposium, 99-104 , 2014 2014 Citations: 32
Automated seasonal separation of mine and non mine water bodies from landsat 8 OLI/TIRS using clay mineral and iron oxide ratio J Mukherjee, J Mukherjee, D Chakravarty IEEE Journal of Selected Topics in Applied Earth Observations and Remote … , 2019 2019 Citations: 19
Investigation of seasonal separation in mine and non mine water bodies using local feature analysis of landsat 8 OLI/TIRS images J Mukherjee, J Mukhopadhyay, D Chakravarty IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium … , 2018 2018 Citations: 14
Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fields J Mukherjee, J Mukherjee, D Chakravarty, S Aikat Multimedia Tools and Applications 80 (28), 35605-35627 , 2021 2021 Citations: 12
Chitosan as an elicitor in plant tissue cultures: Methodological challenges M Roy Chowdhury, M Mehmet, J Mukherjee, AJ Debnath, K Ražná Molecules 30 (17), 3476 , 2025 2025 Citations: 11
Deciphering miRNA‐lncRNA‐mRNA interaction through experimental validation of miRNAs, lncRNAs, and miRNA targets on mRNAs in Cajanus cajan MR Chowdhury, C Chatterjee, D Ghosh, J Mukherjee, S Shaw, J Basak Plant Biology , 2024 2024 Citations: 11
Automated seasonal detection of coal surface mine regions from landsat 8 oli images J Mukherjee, J Mukhopadhyay, D Chakravarty, S Aikat IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium … , 2019 2019 Citations: 11
Detection of coal seam fires in summer seasons from Landsat 8 OLI/TIRS in Dhanbad J Mukherjee, J Mukherjee, D Chakravarty National Conference on Computer Vision, Pattern Recognition, Image … , 2017 2017 Citations: 8
Ontology-driven content-based retrieval of heritage images D Podder, J Mukherjee, SM Aswatha, J Mukherjee, S Sural Heritage Preservation: A Computational Approach, 143-160 , 2018 2018 Citations: 7
Leveraging sentinel-2 data and machine learning for drought detection in India: The process of ground truth construction and a case study SS Sharma, J Mukherjee, F Dell’Acqua Remote Sensing 17 (18), 3159 , 2025 2025 Citations: 5
Unsupervised detection of active, new, and closed coal mines with reclamation activity from landsat 8 oli/tirs images J Mukherjee, J Mukherjee, D Chakravarty, S Aikat International Conference on Pattern Recognition and Machine Intelligence … , 2019 2019 Citations: 5
EchoCNN-Denoiser: a reservoir computing inspired deep learning model for enhanced synthetic aperture radar image despeckling SA Twinkle, S Kamilya, J Mukherjee Journal of Applied Remote Sensing 19 (2), 026501-026501 , 2025 2025 Citations: 4
A study on automated detection of surface and sub-surface coal seam fires using isolation forest from Landsat 8 OLI/TIRS images J Mukherjee IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium … , 2022 2022 Citations: 4
A study on performance and applicability of coal mine index in different surface mining regions J Mukherjee, J Mukhopadhyay, D Chakravarty IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium … , 2022 2022 Citations: 4
Automated coastline detection from Landsat 8 Oli/Tirs images with the presence of inland water bodies in andaman R Mondal, J Mukherjee, J Mukhopadhyay IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020 2020 Citations: 4
Automated detection of mine water bodies using Landsat 8 OLI/TIRS in Jharia J Mukherjee, J Mukherjee, D Chakravarty National Conference on Computer Vision, Pattern Recognition, Image … , 2019 2019 Citations: 4
Duplication detection for image sharing systems J Mukherjee, SM Aswatha, P Mondal, J Mukherjee, P Mitra Proceedings of the 2014 Indian Conference on Computer Vision Graphics and … , 2014 2014 Citations: 4
Real-time retrieval system for heritage images S Mishra, J Mukherjee, P Mondal, SM Aswatha, J Mukherjee Emerging Research in Electronics, Computer Science and Technology … , 2013 2013 Citations: 4
Detection of narrow river trails with the presence of highways from landsat 8 oli images J Mukherjee, P Gupta, H Gautam, R Chintalapati International Conference on Computer Vision and Image Processing, 659-673 , 2022 2022 Citations: 3
