Fertilizer Recommendation Using Machine Learning
  • Author(s): Shreyas Palsapure ; Saurabh Gundecha ; Mazhar Sayyed
  • Paper ID: 1703380
  • Page: 199-201
  • Published Date: 07-05-2022
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 5 Issue 10 April-2022
Abstract

Soil plays a significant role in agriculture and therefore the nutrients on the soil encompasses a direct impact on quality of crops growing thereon. each crop desires associate applicable combination of nutrients to live and grow healthy. Excessive quantity in plant food will be ototoxic to plant development whereas scarce nutrient level may cause sickness to plants. Nutrients in soil is classed in to 2 categories: macronutrients and micronutrients. Macronutrients includes Nitrogen, Phosphorus and K as primary parts and Sulfur, metallic element and metallic element as secondary parts. These area unit the weather that area unit required in comparatively giant amounts. whereas micronutrients embrace iron, boron, manganese, zinc, copper, gas and metallic element. These are also referred to as trace parts as a result of trace quantity of these parts is required by plants. Macronutrients especially N, Phosphorus and K or NPK is commonly the premise of the fertilizers because the 3 numbers that can be seen on a plant food label, indicates the proportion of each macronutrient that the plant food contains. Soil takes a look at is conducted to spot the nutrient level of the soil. There area unit totally different soil takes a look at techniques used and results of this take a look at will be analyzed to return up with a plant food recommendation applicable for a definite crop. The nutrient deficiency of the soil should be addressed by relating the result to the expected level of nutrient. Nutrient level additionally changes as the weather changes on dry or wet season. The objective of this paper is to style a symbolic logic program that may offer plant food recommendation supported the season and Nitrogen-Phosphorus-Potassium (NPK) level of the soil.

Citations

IRE Journals:
Shreyas Palsapure , Saurabh Gundecha , Mazhar Sayyed "Fertilizer Recommendation Using Machine Learning" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 199-201

IEEE:
Shreyas Palsapure , Saurabh Gundecha , Mazhar Sayyed "Fertilizer Recommendation Using Machine Learning" Iconic Research And Engineering Journals, 5(10)