Work

Crop Suggestion and Disease Detection System using Random Forest and Convolutional Neural Network

Year: 2025

Type: article

Abstract: : This study provides an extensive web and mobile-based application aimed at improving agriculture practice using crop suggestion, disease prediction, and farmer query answering. The system applies machine learning, image processing, and natural language processing concepts to present recommendatory information to farmers. Random Forest is applied for crop prediction with an accuracy rate of 99.14%, while CNN is applied for disease prediction. Moreover, an expert-consulting system through real-time chat-based query resolution links farmers with experts. This technology-based solution is designed to enhance crop yield, optimize farm decisions, and increase accessibility for farmers

Source: International Journal for Research in Applied Science and Engineering Technology

DOI: 10.22214/ijraset.2025.69943

Authors: Purva Singh, Sanskrita Tewari, Ratan Rajan, Shivam Dixit

Institutions: Shri Ramswaroop Memorial University, Shri Ramswaroop Memorial College of Engineering and Management

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

OpenAlex ID: W4410157724

FWCI:

Cites: 10

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Related to: 10