An Efficient Decision Making system for Sustainable Fertilization

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KONGR

Efficient Decision-Making for Sustainable Fertilization.

An Efficient Decision Making system for Sustainable Fertilization

Girish Saunshi
Girish Saunshi
Dr. Rajesh Yakkundimath
Dr. Rajesh Yakkundimath
Shridhar Chini
Shridhar Chini
Dr. M. C.Elemmi
Dr. M. C.Elemmi
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T
DOI

Abstract

Farmers often face challenges in effectively managing fertilizer use and must rely on expert advice to maximize yields while minimizing fertilizer waste. Precipitation plays an essential role in the loss of nutrients after each rainfall event. Timely rainfall can help nutrients penetrate into the root zone of the soil and dissolve dry fertilizer, thereby improving nutrient absorption. However, excessive rainfall increases the risk of runoff, leading to the loss of key nutrients such as nitrogen (N), essential elements such as phosphorus (P) and potassium (K), and other nutrients such as manganese (Mn) and boron (B). Of the ground. The study used time-series data on rainfall and crop fertility. It uses an improved version of the random forest algorithm to predict the optimal nutritional needs of different crops. The method proposed in this study aims to improve soil fertility by offering nutrient recommendations that promote ideal crop growing conditions while minimizing leaching and runoff. nutrient overflow.

An Efficient Decision Making system for Sustainable Fertilization

Farmers often face challenges in effectively managing fertilizer use and must rely on expert advice to maximize yields while minimizing fertilizer waste. Precipitation plays an essential role in the loss of nutrients after each rainfall event. Timely rainfall can help nutrients penetrate into the root zone of the soil and dissolve dry fertilizer, thereby improving nutrient absorption. However, excessive rainfall increases the risk of runoff, leading to the loss of key nutrients such as nitrogen (N), essential elements such as phosphorus (P) and potassium (K), and other nutrients such as manganese (Mn) and boron (B). Of the ground. The study used time-series data on rainfall and crop fertility. It uses an improved version of the random forest algorithm to predict the optimal nutritional needs of different crops. The method proposed in this study aims to improve soil fertility by offering nutrient recommendations that promote ideal crop growing conditions while minimizing leaching and runoff. nutrient overflow.

Girish Saunshi
Girish Saunshi
Dr. Rajesh Yakkundimath
Dr. Rajesh Yakkundimath
Shridhar Chini
Shridhar Chini
Dr. M. C.Elemmi
Dr. M. C.Elemmi
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T

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Girish Saunshi. 2026. “. Global Journal of Computer Science and Technology – D: Neural & AI GJCST-D Volume 23 (GJCST Volume 23 Issue D3): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Issue Cover
GJCST Volume 23 Issue D3
Pg. 19- 25
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GJCST-D Classification: FoR: 0502
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An Efficient Decision Making system for Sustainable Fertilization

Girish Saunshi
Girish Saunshi
Dr. Rajesh Yakkundimath
Dr. Rajesh Yakkundimath
Shridhar Chini
Shridhar Chini
Dr. M. C.Elemmi
Dr. M. C.Elemmi
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T
Dr. Yerriswamy T

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