Yaganteeswarudu Akkem

@.deloitte.com

Data scientist
Deloitte

Yaganteeswarudu Akkem


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https://researchid.co/yaganteeswarudu

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Software
16

Scopus Publications

1292

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Deciphering the black box: interactive crop recommendation system using Explainable AI with visualisation dashboards
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Varanasi Aruna
    Journal of Experimental and Theoretical Artificial Intelligence, 2026
    The integration of Artificial Intelligence (AI) into smart farming, particularly Crop Recommendation System (CRS), has propelled significant advancements but is often hindered by the ‘black box’ nature of models, which limits transparency and trust. This study aims to enhance smart farming by embedding explainable Artificial Intelligence (XAI) techniques – specifically Contrastive Explanation Method (CEM) and Accumulated Local Effects (ALE) – within CRS, empowering farmers to understand AI-generated crop suggestions. Implemented an XAI-driven CRS, utilising Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), CEM, and ALE for comprehensive explainability. Notably, CEM provides farmers with actionable contrastive explanations, while ALE details the average influence of environmental factors. To address data scarcity, Generative Adversarial Networks (GANs) were used to augment the dataset with synthetic data, and an interactive, explainable interface was developed using Streamlit. Results show a substantial improvement in system interpretability and user trust, evidenced by clearer, actionable explanations for farmers. Quantitatively, incorporating GAN-augmented data improved the Random Forest model’s Area Under the Receiver Operating Characteristic curve (AUROC) from 0.94 to 0.985 and F1-score from 0.93 to 0.98. This research is the first to integrate CEM and ALE in CRS, establishing a new benchmark for transparent and effective AI-powered agricultural decision-making.
  • AI-Driven smart farming portal: overcoming language barriers and enhancing agricultural productivity through machine learning
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    Engineering Research Express, 2025
    The Smart Farming Portal leverages Artificial Intelligence (AI) to address language barriers and enhance agricultural productivity for illiterate farmers. By employing machine learning (ML) models, the portal predicts crop yield, recommends suitable crops, and assesses soil fertility. It integrates Google Translate API to provide multilingual support, ensuring accessibility for farmers across various languages. The portal consolidates essential agricultural predictions into a user-friendly interface, empowering farmers to make informed decisions and optimize farming practices. This initiative aims to improve crop selection and yield, ultimately boosting agricultural productivity.
  • Analysis of An Intellectual Mechanism of a Novel Crop Recommendation System Using Improved Heuristic Algorithm-Based Attention and Cascaded Deep Learning Network
    Yaganteeswarudu Akkem, Saroj Kumar Biswas
    IEEE Transactions on Artificial Intelligence, 2025
    This article introduces an innovative crop recommendation system that leverages an attention-based cascaded deep learning network (AACNet) optimized by an improved migration algorithm (IMA). The system is designed to address the inefficiencies of traditional crop recommendation methods by providing precise, real-time suggestions tailored to specific agricultural factors such as weather, soil type, and time. The AACNet employs recurrent neural networks (RNN) and gated recurrent units (GRU) to analyze time-sensitive agricultural factors, such as weather patterns and soil conditions, while the attention mechanism prioritizes the most significant features for accurate crop recommendations. The IMA optimizes the deep learning network, enhancing the system’s accuracy, precision, recall, and execution time. Experimental results demonstrate that the proposed system outperforms traditional methods, marking a significant advancement in precision agriculture. The system’s potential to revolutionize farming decision-making processes by optimizing resource allocation, reducing costs, and increasing crop yields underscores its importance in global agricultural challenges. This research represents a transformative step towards informed, efficient, and sustainable farming practices.
  • Role of Explainable AI in Crop Recommendation Technique of Smart Farming
    , Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    International Journal of Intelligent Systems and Applications, 2025
    Smart farming is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence (AI) to improve crop recommendations. Despite the advancements, a critical gap exists in opaque ML models that need to explain their predictions, leading to a trust deficit among farmers. This research addresses the gap by implementing explainable AI (XAI) techniques, specifically focusing on the crop recommendation technique in smart farming. An experiment was conducted using a Crop recommendation dataset, applying XAI algorithms such as Local Interpretable Model-agnostic Explanations (LIME), Differentiable InterCounterfactual Explanations (dice_ml), and SHapley Additive exPlanations (SHAP). These algorithms were used to generate local and counterfactual explanations, enhancing model transparency in compliance with the General Data Protection Regulation (GDPR), which mandates the right to explanation. The results demonstrated the effectiveness of XAI in making ML models more interpretable and trustworthy. For instance, local explanations from LIME provided insights into individual predictions, while counterfactual scenarios from dice_ml offered alternative crop cultivation suggestions. Feature importance from SHAP gave a global perspective on the factors influencing the model's decisions. The study's statistical analysis revealed that the integration of XAI increased the farmers' understanding of the AI system's recommendations, potentially reducing food insufficiency by enabling the cultivation of alternative crops on the same land.
  • Enhancing Transparency in Smart Farming: Local Explanations for Crop Recommendations Using LIME
    A Yaganteeswarudu, Saroj Kumar Biswas, Varanasi Aruna, Deeksha Tripathi
    Procedia Computer Science, 2025
    The integration of Explainable Artificial Intelligence (XAI) into smart farming enhances Crop Recommendation (CR) systems by improving transparency and understanding through local explanations. This paper discusses developing and implementing an XAI-enabled CR system using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The system builds user trust and supports informed agricultural decision-making by offering local explanations for individual records. Using LIME, the system elucidates the impact of various features on crop predictions, assigning positive or negative values to indicate their contributions. A positive LIME value marks a feature’s favorable impact, while a negative value indicates a detrimental effect. The paper highlights the system’s effectiveness with detailed crop explanations. For example, nitrogen, humidity, and rainfall positively influenced coffee, while for lentils, rainfall, nitrogen, and potassium were significant.
  • Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    Neural Computing and Applications, 2024
  • A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    Engineering Applications of Artificial Intelligence, 2024
  • Crop Recommendation System Using Machine Learning and IoT: A Survey
    Kishan Babu Kancharagunta, Yaganteeswarudu Akkem, Madhu Vembadi, Shravan Teja Garalapati, A. Hari Pratap Varma, M. Ruha Jessica
    Lecture Notes in Networks and Systems, 2024
  • Smart farming using artificial intelligence: A review
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    Engineering Applications of Artificial Intelligence, 2023
  • Smart Farming Monitoring Using ML and MLOps
    Yaganteeswarudu Akkem, Saroj Kumar Biswas, Aruna Varanasi
    Lecture Notes in Networks and Systems, 2023
  • Diabetes analysis and risk calculation – auto rebuild model by using flask api
    Akkem Yaganteeswarudu, Prabhakar Dasari
    Advances in Intelligent Systems and Computing, 2021
  • Security in Software Applications by Using Data Science Approaches
    Akkem Yaganteeswarudu, Aruna Varanasi, Sangeet Mohanty
    Lecture Notes in Networks and Systems, 2021
  • Multi disease prediction model by using machine learning and Flask API
    Proceedings of the 5th International Conference on Communication and Electronics Systems Icces 2020, 2020
  • The multi language audio compiler with video help
    A. Yaganteeswarudu, B.Vasundhara Devi
    2018 3rd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Rteict 2018 Proceedings, 2018
  • Software appication to prevent suicides of farmers with ASP.net MVC
    A. Yaganteeswarudu, Y VishnuVardhan
    Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing Data Science and Engineering, 2017
  • The speaking compiler-A compiler with audio for immediate error correction
    Proceedings of the 10th Indiacom 2016 3rd International Conference on Computing for Sustainable Global Development Indiacom 2016, 2016

RECENT SCHOLAR PUBLICATIONS

  • Deciphering the black box: interactive crop recommendation system using Explainable AI with visualisation dashboards
    AV Yaganteeswarudu Akkem,Saroj Kumar Biswas
    Journal of Experimental & Theoretical Artificial Intelligence, 1-41 , 2025
    2025
    Citations: 1
  • Ai-driven smart farming portal: overcoming language barriers and enhancing agricultural productivity through machine learning
    Y Akkem, SK Biswas, A Varanasi
    Engineering Research Express 7 (2), 025288 , 2025
    2025
    Citations: 10
  • Role of Explainable AI in Crop Recommendation Technique of Smart Farming
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    International Journal of Intelligent Systems and Applications (IJISA) 17 , 2025
    2025
    Citations: 34
  • Design of improved agriculture crop recommendation with global-local interpretability
    Y Akkem, SK Biswas, A Varanasi
    Istor. J. 8 (9), 42-60 , 2025
    2025
    Citations: 4
  • Enhancing Transparency in Smart Farming: Local Explanations for Crop Recommendations Using LIME
    DT Akkem Yaganteeswarudu, Saroj Kumar Biswas, Aruna Varanasi
    Procedia Computer Science 258, 1993-2005 , 2025
    2025
    Citations: 28
  • Analysis of an intellectual mechanism of a novel crop recommendation system using improved heuristic algorithm-based attention and cascaded deep learning network
    Y Akkem, SK Biswas
    IEEE Transactions on Artificial Intelligence 6 (5), 1100-1113 , 2024
    2024
    Citations: 33
  • Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Neural Computing and Applications , 2024
    2024
    Citations: 103
  • AN ARTIFICIAL INTELLIGENCE BASED EXPLAINABLE CROP RECOMMENDATION SYSTEM
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    ZA Patent 2024/01,704 , 2024
    2024
  • Crop recommendation system using machine learning and iot: A survey
    KB Kancharagunta, Y Akkem, M Vembadi, ST Garalapati, ...
    International Conference On Innovative Computing And Communication, 63-86 , 2024
    2024
    Citations: 7
  • A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Engineering Applications of Artificial Intelligence 131 , 2024
    2024
    Citations: 292
  • Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 16 (48), 4688-4702 , 2023
    2023
    Citations: 107
  • Smart farming monitoring using ML and MLOps
    Y Akkem, SK Biswas, A Varanasi
    International Conference On Innovative Computing And Communication, 665-675 , 2023
    2023
    Citations: 50
  • Smart farming using artificial intelligence: A review
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Engineering Applications of Artificial Intelligence 120 , 2023
    2023
    Citations: 477
  • Security in software applications by using Data Science Approaches
    A Yaganteeswarudu, A Varanasi, S Mohanty
    Proceedings of International Conference on Sustainable Expert Systems: ICSES … , 2021
    2021
    Citations: 3
  • Multi disease prediction model by using machine learning and Flask API
    A Yaganteeswarudu
    2020 5th International conference on communication and electronics systems … , 2020
    2020
    Citations: 109
  • Diabetes analysis and risk calculation–auto rebuild model by using flask api
    A Yaganteeswarudu, P Dasari
    International conference on image processing and capsule networks, 299-308 , 2020
    2020
    Citations: 14
  • The multi language audio compiler with video help
    A Yaganteeswarudu, BV Devi
    2018 3rd IEEE International Conference on Recent Trends in Electronics … , 2018
    2018
    Citations: 2
  • Software appication to prevent suicides of farmers with asp. net MVC
    A Yaganteeswarudu, Y VishnuVardhan
    2017 7th international conference on cloud computing, data science … , 2017
    2017
    Citations: 15
  • The speaking compiler-A compiler with audio for immediate error correction
    A Yaganteeswarudu
    2016 3rd International Conference on Computing for Sustainable Global … , 2016
    2016
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Smart farming using artificial intelligence: A review
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Engineering Applications of Artificial Intelligence 120 , 2023
    2023
    Citations: 477
  • A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Engineering Applications of Artificial Intelligence 131 , 2024
    2024
    Citations: 292
  • Multi disease prediction model by using machine learning and Flask API
    A Yaganteeswarudu
    2020 5th International conference on communication and electronics systems … , 2020
    2020
    Citations: 109
  • Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 16 (48), 4688-4702 , 2023
    2023
    Citations: 107
  • Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    Neural Computing and Applications , 2024
    2024
    Citations: 103
  • Smart farming monitoring using ML and MLOps
    Y Akkem, SK Biswas, A Varanasi
    International Conference On Innovative Computing And Communication, 665-675 , 2023
    2023
    Citations: 50
  • Role of Explainable AI in Crop Recommendation Technique of Smart Farming
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    International Journal of Intelligent Systems and Applications (IJISA) 17 , 2025
    2025
    Citations: 34
  • Analysis of an intellectual mechanism of a novel crop recommendation system using improved heuristic algorithm-based attention and cascaded deep learning network
    Y Akkem, SK Biswas
    IEEE Transactions on Artificial Intelligence 6 (5), 1100-1113 , 2024
    2024
    Citations: 33
  • Enhancing Transparency in Smart Farming: Local Explanations for Crop Recommendations Using LIME
    DT Akkem Yaganteeswarudu, Saroj Kumar Biswas, Aruna Varanasi
    Procedia Computer Science 258, 1993-2005 , 2025
    2025
    Citations: 28
  • Software appication to prevent suicides of farmers with asp. net MVC
    A Yaganteeswarudu, Y VishnuVardhan
    2017 7th international conference on cloud computing, data science … , 2017
    2017
    Citations: 15
  • Diabetes analysis and risk calculation–auto rebuild model by using flask api
    A Yaganteeswarudu, P Dasari
    International conference on image processing and capsule networks, 299-308 , 2020
    2020
    Citations: 14
  • Ai-driven smart farming portal: overcoming language barriers and enhancing agricultural productivity through machine learning
    Y Akkem, SK Biswas, A Varanasi
    Engineering Research Express 7 (2), 025288 , 2025
    2025
    Citations: 10
  • Crop recommendation system using machine learning and iot: A survey
    KB Kancharagunta, Y Akkem, M Vembadi, ST Garalapati, ...
    International Conference On Innovative Computing And Communication, 63-86 , 2024
    2024
    Citations: 7
  • Design of improved agriculture crop recommendation with global-local interpretability
    Y Akkem, SK Biswas, A Varanasi
    Istor. J. 8 (9), 42-60 , 2025
    2025
    Citations: 4
  • Security in software applications by using Data Science Approaches
    A Yaganteeswarudu, A Varanasi, S Mohanty
    Proceedings of International Conference on Sustainable Expert Systems: ICSES … , 2021
    2021
    Citations: 3
  • The speaking compiler-A compiler with audio for immediate error correction
    A Yaganteeswarudu
    2016 3rd International Conference on Computing for Sustainable Global … , 2016
    2016
    Citations: 3
  • The multi language audio compiler with video help
    A Yaganteeswarudu, BV Devi
    2018 3rd IEEE International Conference on Recent Trends in Electronics … , 2018
    2018
    Citations: 2
  • Deciphering the black box: interactive crop recommendation system using Explainable AI with visualisation dashboards
    AV Yaganteeswarudu Akkem,Saroj Kumar Biswas
    Journal of Experimental & Theoretical Artificial Intelligence, 1-41 , 2025
    2025
    Citations: 1
  • AN ARTIFICIAL INTELLIGENCE BASED EXPLAINABLE CROP RECOMMENDATION SYSTEM
    AV Yaganteeswarudu Akkem, Saroj Kumar Biswas
    ZA Patent 2024/01,704 , 2024
    2024

Publications

Yaganteeswarudu Akkem, Saroj Kr. Biswas, Aruna Varanasi, ‘A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network’, Engineering Applications of Artificial Intelligence, Elsevier, (SCI journal, IF: 8.0)
Yaganteeswarudu Akkem, Saroj Kr. Biswas, Aruna Varanasi, ‘Smart farming using artificial intelligence: A Review’, Engineering Applications of Artificial Intelligence, Elsevier, (SCI journal, IF: 8.0)
Paper published in Springer conference ICICC-2023 , titled “Smart farming monitoring using ML and MLOps ”
 Published a paper in IEEE explore on “Multi disease prediction model by using Flask API”
 Attended IEEE conference, International conference on Communication and Electronics (ICCES-2020) on “Multi disease prediction model by using Flask API”
 Published a paper in IEEE explore and Attended IEEE Conference at BVICAM at Delhi on March 2016 on “speaking compiler- a compiler with audio for immediate error
Published IEEE explore paper and Attended IEEE conference at Amity University at Noida on January 2017 on “software application to prevent suicides of farmers by using
Published IEEE explore paper and attended IEEE conference on “Multilanguage audio compiler with video help”
Published a paper in springer and international conference on sustainable expert systems, ICSES 2020 titled “security in software applications by using data science approaches”