ArticleScientific reports2026
Improving precision agriculture using integrated bio-inspired optimization models for crop recommendation in Rajasthan, India.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI.Frontiers in artificial intelligence · 2026Article
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4 authors.
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Abstract
This study introduces two novel hybrid nature-inspired optimization algorithms designed to enhance artificial neural network (ANN) performance in crop recommendation, leveraging remote sensing data from Landsat 8 and 9 platforms. The first hybrid approach combines the Gravitational Search Algorithm (GSA) with the Hunger Games Search (HGS) algorithm, promoting an improved balance between exploration and exploitation through gravitational dynamics and competitive resource-seeking strategies. The second hybrid integrates Electric Eel Foraging Optimization (EEFO) with Crested Porcupine Optimization (CPO), leveraging the foraging adaptability of electric eels and the defensive spatial strategies of crested porcupines to refine search efficiency and convergence stability. These hybrid algorithms were applied to classify crops across Kharif and Rabi seasons in Rajasthan, India. Experimental results reveal that the GSA-HGS hybrid achieves classification accuracies of 95.32% for Kharif and 94.99% for Rabi seasons, while the EEFO-CPO hybrid attains 94.09% and 94.94%, respectively. These findings demonstrate the potential of bio-inspired optimization strategies to support intelligent crop recommendation systems and advance precision agriculture practices in data-scarce agricultural regions.
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