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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Degraded and Mining Lands Management
Hediyalad, Gangamma
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering

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A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation Hediyalad, Gangamma; Ashoka , K; Hegade, Govardhan; Gaonkar, Pratibha Ganapati; Pathan, Azizkhan F; Malagatti, Pratibhaa R
Journal of Degraded and Mining Lands Management Vol. 11 No. 4 (2024)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2024.114.6471

Abstract

Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning algorithms. Initially, the current status of land degradation in India is presented, along with a brief discussion on the overview of widely used factors, evaluation parameters, and algorithms used. Consequently, merits and demerits related to machine learning-based land degradation identification are presented. Additionally, solutions are prescribed in order to reduce existing problems in the detection of land degradation. Since one of the major objectives is to explore the future perspectives of machine learning-based land degradation detection, areas including the application of remote sensing, mapping, optimum features, and algorithms have been broadly discussed. Finally, based on a critical evaluation of existing related studies, the architecture of the machine learning-based desertification process has been proposed. This technology can fulfill the research challenges in the detection of land degradation and computation difficulties in the development of models for the detection of land degradation.
Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience Hediyalad, Gangamma; Kukkuvada, Ashoka; Hegde Kota, Govardhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3133-3142

Abstract

This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.
Climate change and pollinator dynamics: integrating social media insights and ecological data for conservation strategies Hadimane, Pooja; Kukkuvada, Ashoka; Hediyalad, Gangamma; Hegade Kota, Govardhan; Kisan, Rajeswari; Patil, Shivanand; Myala, Arjun; Anekonda Subhash, Basavaraja
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1680-1690

Abstract

Pollination is an essential ecosystem service intricately linked to biodiversity, ecosystem health, and agricultural systems. The need to understand the effect of climate change on pollination processes has never been greater, given that a significant portion of global crop production is dependent on biotic pollination. This survey paper examines the multifaceted challenges that climate change poses to pollination dynamics across various ecosystems. By synthesizing existing literature to highlight how alterations in temperature and precipitation patterns have led to a phenological mismatch between pollinators and plants, potentially disrupting established trophic relationships and ecosystem functions. Our review reveals that insect-pollinated plants, particularly those that bloom early in the season, exhibit a heightened sensitivity to climate-induced phenological shifts. Moreover, exploring how the altered life cycles of pollinators, struggling to synchronize with the new flowering schedules, may precipitate declines in pollination services. Our findings underscore the critical need for conservation strategies that address climate adaptation for pollinators, focusing on enhancing landscape connectivity and heterogeneity. By bridging diverse studies ranging from the application of social media data in ecological research to advanced predictive models for pollination services, the main aim is to foster a deeper understanding of the consequences of climate change on pollination.
Co-Authors Anekonda Subhash, Basavaraja Ashoka , K Gaonkar, Pratibha Ganapati Hadimane, Pooja Hegade Kota, Govardhan Hegade, Govardhan Hegde Kota, Govardhan Kisan, Rajeswari Kukkuvada, Ashoka Malagatti, Pratibhaa R Myala, Arjun Pathan, Azizkhan F Patil, Shivanand