An Advance Food Ordering System Using QR Code

Abstract

Crop prediction plays a crucial role in
modern agriculture by helping farmers make
informed decisions about what crops to plant,
ensuring optimal yields, and reducing resource
wastage. This study explores the application of
machine learning algorithms, specifically
Random Forest, Decision Tree, and Passive-
Aggressive algorithms, for predicting the bestsuited
crop based on various environmental and
soil parameters. The input features considered for
prediction include temperature, humidity, pH,
rainfall, and soil nutrients (Nitrogen, Phosphorus,
Potassium), while the output is the recommended
crop name. A dataset consisting of these
parameters was used to train and evaluate the
models. The performance of each algorithm was
compared based on their accuracy in correctly
predicting the appropriate crop. Results indicate
that machine learning models, especially Random
Forest, show promising results in crop prediction
by effectively utilizing environmental and soil data
to provide accurate recommendations. This
approach offers a scalable solution for precision
agriculture, helping farmers optimize crop
selection, improve productivity, and manage
resources more efficiently.