Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
In an era
marked by the escalating demand for sustenance, the agricultural sector
shoulders the pivotal responsibility of nourishing an ever-expanding global
population. However, the industry grapples with the pervasive issue of
agricultural losses, which reverberates across production stages, imperiling
both food security and economic stability. To mitigate this challenge, the
present research endeavors to forge a path towards agricultural resilience by
introducing an innovative application that harnesses the untapped potential of
advanced machine learning models. This groundbreaking application is
meticulously tailored to empower farmers with incisive predictions and
strategic counsel, aimed at optimizing crop yield and mitigating wastage. The
study’s core premise converges on four pivotal components: Agricultural Demand
Prediction, Yield Projection, Destination-Driven Crop Selection, Disease
Detection via image processing, and a fertilizer recommendation. This
multifaceted framework encapsulates the quintessence of precision agriculture,
coalescing technology, and agribusiness acumen. With the reduction of
agricultural losses as its lodestar, the application illuminates a
transformative trajectory towards sustainable farming practices, cultivating a
future where limited resources are adeptly channeled to yield bountiful yet judicious
harvests.
Country : Sri Lanka
IRJIET, Volume 7, Issue 10, October 2023 pp. 33-41
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analytics.," Journal of Agricultural Informatics., 2020. |
