Authors :
Prabavathi R; Subha P; Bhuvaneswari M; Prithisha V; Roshini K
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/2drru9u9
Scribd :
https://tinyurl.com/3xhncf2s
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR532
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Agricultural productivity hinges on soil
fertility, influenced by key factors like nitrogen,
phosphorus, potassium, pH level, and soil moisture. Yet,
achieving optimal crop growth is challenging due to
limited farmer knowledge and difficulties in determining
precise fertilizer quantities. Conventional soil analysis
methods involve manual sampling andcostly lab tests,
which are subjective. To address this, aproposed solution
integrates IoT-enabled soil nutrient monitoring with
machine learning algorithms for croprecommendations.
Sensors collect data on crucial parameters like nitrogen,
phosphorus, and soil temperature, transmitting it to a
cloud-based database. Machine learning analyzes this
data to suggest ideal crops, minimizing fertilizer use,
reducing labor, and enhancing overall productivity. This
innovative approach streamlines crop selection,
minimizing unnecessary inputs while maximizing yields.
By harnessing IoT and machine learning, farmers gain
valuable insights into soil health, enabling precise
fertilization and crop selection. This not only boosts
agricultural productivity but also contributes to economic
growth by fostering sustainable practices andincreased
yields.
Keywords :
Agriculture Yields, Crop Recommendation, Machine Learning, Soil Behavior Analysis.
Agricultural productivity hinges on soil
fertility, influenced by key factors like nitrogen,
phosphorus, potassium, pH level, and soil moisture. Yet,
achieving optimal crop growth is challenging due to
limited farmer knowledge and difficulties in determining
precise fertilizer quantities. Conventional soil analysis
methods involve manual sampling andcostly lab tests,
which are subjective. To address this, aproposed solution
integrates IoT-enabled soil nutrient monitoring with
machine learning algorithms for croprecommendations.
Sensors collect data on crucial parameters like nitrogen,
phosphorus, and soil temperature, transmitting it to a
cloud-based database. Machine learning analyzes this
data to suggest ideal crops, minimizing fertilizer use,
reducing labor, and enhancing overall productivity. This
innovative approach streamlines crop selection,
minimizing unnecessary inputs while maximizing yields.
By harnessing IoT and machine learning, farmers gain
valuable insights into soil health, enabling precise
fertilization and crop selection. This not only boosts
agricultural productivity but also contributes to economic
growth by fostering sustainable practices andincreased
yields.
Keywords :
Agriculture Yields, Crop Recommendation, Machine Learning, Soil Behavior Analysis.
