Crop recommendation and crop disease detection using artificial intelligence (ai) and internet of things (iot)
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
designed to empower farmers with data-driven insights for sustainable agriculture. This
system integrates loT, machine learning (ML), and deep learning (DL) technologies to
monitor field conditions, recommend suitable crops, and identify crop diseases
accurately. Real-time field data is collected through loT sensors, which measure essential
parameters such as soil moisture, temperature, pH, humidity, and sunlight exposure. The
system also incorporates nutrient data (N, P, K) from laboratory reports to enhance
accuracy. External weather data from APls and historical crop performance metrics are
integrated into the dataset, enabling location-specific predictions and insights. The data
transmission framework uses loT sensors to send field data to a central gateway. This
gateway consolidates sensor inputs and securely transmits them to a cloud or edge
server for storage and processing. Preprocessing steps such as data cleaning,
normalization, and feature engineering ensure high-quality data for analysis.
Normalization makes data such as pH and nutrient levels comparable, while feature
engineering derives additional insights like soil moisture trends, temperature
fluctuations, and drought probabilities. These refined datasets support robust model
training and analysis. The ML model leverages soil and environmental data to create a
crop suitability profile, recommending optimal crops for planting. Decision trees, random
forests, and neural networks trained on labeled datasets provide accurate predictions. A
separate deep learning model, built using convolutional neural networks (CNNs), is
designed to detect and classify crop diseases based on leaf images. The PlantVillage
dataset from Kaggle serves as the training data for this model, while the HDFS library
ensures efficient storage and retrieval of large datasets during training and inference. A
Flask-based backend system seamlessly integrates the ML and DL models, managing data
flow between loT sensors, cloud storage, and the user interface. This backend processes
user requests, executes model predictions, and delivers actionable insights. The client
side interface, developed using HTML and CSS, presents soil recommendations, and disease detection results in an accessible asnodil udsaetra-f, riencdrolyp
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