Abstract: Agricultural productivity and sustainability are vital for global food security, but challenges like soil degradation, crop diseases, and inefficient fertilizer use hinder crop quality and yield. To address these, advanced technologies like machine learning and AI are increasingly used in agriculture. This review focuses on recent advances in soil prediction, crop disease prediction, and fertilizer recommendation systems. Soil prediction models assess nutrient content and pH levels using data sources like satellite imagery and historical records, aiding precise soil management. Crop disease prediction systems use AI to identify and forecast disease outbreaks, leading to early warnings and reduced agrochemical use. Fertilizer recommendation systems employ machine learning to suggest optimized fertilizer usage, enhancing efficiency and reducing environmental impact and costs.
AI integration has the potential to transform agriculture, promoting sustainability and higher yields. Ongoing research and interdisciplinary collaboration are needed to overcome challenges related to data accessibility and technology integration. Harnessing AI-driven solutions can lead to a more resilient and sustainable agricultural ecosystem, combining advanced technology with traditional agricultural wisdom to ensure a food-secure future.
Keywords: Agricultural productivity, Crop diseases, Crop yields, Fertilizer, Soil , Crop disease.
Cite:
Ayushi Gajbhiye,Madhav Murkute, Najuka Anjankar, Ayush Hedaoo,Prof.Virendra Yadav, "Soil, Disease Prediction & Fertilizer Recommendation", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13123.
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DOI:
10.17148/IJARCCE.2024.13123
