Smart Soil Analyzer and Crop Guidance System
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Science
Abstract
This study aims to develop an intelligent crop recommendation system based on soil
characteristics using artificial intelligence techniques. The core objective is to classify
the most suitable crop for cultivation in a specific soil based on various features such
as nitrogen (N), phosphorus (P), potassium (K) levels, pH value, moisture, rainfall,
soil type, and other environmental factors.
To achieve this, we applied a set of machine learning and deep learning algorithms,
including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM),
Logistic Regression (LR), as well as LSTM and GRU neural networks. The models
were trained using a specialized agricultural dataset collected from Kaggle, with
consistent preprocessing and splitting methods to ensure fair performance comparison.
A thorough hyperparameter tuning process was carried out to identify the optimal
settings for each algorithm. The experimental results showed that deep learning
models (LSTM and GRU) achieved strong classification performance, while the Decision
Tree model provided good accuracy with lower computational requirements,
making it a suitable option for resource constrained applications.