Deep Grow: Intelligent Crop & Leaf Diagnosis

Deep Grow: Intelligent Crop & Leaf Diagnosis

 Submitted by:

Mrs.K. ANURANJANI1a, SUVIDARAN T2b,

THARUN M3c, THARUN S4d, THIRUMURTHY S5e

Assistant Professor, Department of CSE Bharath Institute of Higher Education and Research,

Selaiyur, Chennai, Tamil Nadu, India

B. Tech Students, Bharath Institute of Higher Education and Research,

Selaiyur, Chennai, Tamil Nadu, India

Anuranjani.cse@bharatuniv.ac.in , suvidarantamilselvam@gmail.com , tharun0983@gmail.com ,

Kumartharun695@gmail.com , thirumurthy871@gmail.com .

Abstract

To solve important problems in crop recommendation, plant disease detection, increasing agricultural output and guaranteeing sustainable practices Artificial Intelligence (AI) is used in precision agriculture. This study introduces dual-purpose artificial intelligence system that combines a hybrid machine learning model for crop recommendation based on soil characteristics, climate variables, and past yield data with Convolutional Neural Networks (CNN) for plant disease identification. Disease identification model achieves good classification accuracy by using a large dataset of plant pictures. To produce customized crop recommendations, it simultaneously analyses soil pH, nitrogen, phosphorus, potassium, weather data using Random Forest (RF) and Support Vector Machine (SVM) models.

Keywords:  Plant disease, crop recommendation, convolutional neural networks, machine learning, precision agriculture.