Smart Paddy Identification and Soil Suitability Prediction Using Deep Learning Techniques

Abstract

Agriculture plays a vital role in the
economy and food security of many
countries. Paddy is one of the most
important staple crops cultivated
worldwide. Accurate crop detection and
appropriate soil recommendation are
essential for improving yield and
reducing losses. Traditional farming
practices rely heavily on farmer
experience and manual observation.
These methods may lead to inefficient
resource usage and reduced productivity.
Artificial Intelligence provides advanced
solutions for smart agriculture. This
project focuses on paddy crop detection
and soil recommendation using AI
techniques. Image processing and
machine learning models are used to
detect paddy crop conditions. Soil
parameters such as moisture, pH, and
nutrient content are analyzed. The system
recommends suitable soil treatments and
fertilizers. Data is collected from sensors
and crop images. AI models process this
data for accurate prediction. The system
supports early detection of crop issues. It
helps farmers make informed decisions.
Automation reduces manual effort. The
approach improves crop yield and soil
health. The system is scalable and costeffective.
It promotes sustainable farming
practices. The proposed solution
demonstrates the effectiveness of AI in
agriculture