Abstract

Agriculture is crucial to the existence of man, and its productivity in a country plays a significant role in the growth of the economy. Conventionally, farmers used experience to know how to cultivate and the types of crops to grow, but developments in climatic and environmental conditions have, in recent years, complicated the process of selecting the right crop for farmers. This research will answer this question by applying machine learning (ML) methods to make a fruit crop suggestion using soil macronutrients such as Nitrogen, Phosphorus, and Potassium, temperature, humidity, rainfall, and soil pH. Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) were comparatively tested out of 5 ML algorithms. According to experiment results, the predictive accuracy of 95.45 with 5-fold validation of 5-fold validation is the highest, with better ability to interpret features, strong resistance to noise, and high levels of consistency. NPK patterns, climate effects, and correlation-based interactions feature analysis further amplify the accuracy of the model. These findings affirm that ML-based soil-climate fusion has the capability to provide a scalable, interpretable, and effective framework of sustainable fruit crop recommendation in the changing agro-climatic conditions in India.

Keywords

Crop Recommendation, Machine Learning, Agriculture, Cross Validation,

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References

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