Year: 2025
Type: article
Abstract: This Study explores how machine learning (ML) and deep learning (DL) are revolutionizing agriculture, especially in crop prediction and disease detection. The proposed system presents a Smart Farming system powered by AI that is based on integrating machine learning (ML) and deep learning (DL) technologies to transform farm practices. The framework utilizes ML algorithms to read soil characteristics, climate conditions, and weather forecasting for precise prediction of crops, while DL models leverage image-based disease detection algorithms to detect diseases in plants at high accuracy levels. By transforming heterogeneous data sets of soil parameters, climate information, and crop images, important features like soil pH, nutrient content, and leaf status are designed to enhance model performance. The system is focused on real-time monitoring and AI-based suggestions, with the aim of detecting diseases at an early stage and selecting the best crops. Experimental outcomes show improved accuracy and responsiveness over conventional agriculture practices, lowering losses and increasing agricultural productivity significantly. This multi-dimensional strategy not only enhances farmer decision-making but also sets a new standard in smart farming, encouraging sustainability, efficiency, and resilience in contemporary agriculture.
Authors: Vitthal B. Kamble, Jiya Sharma, Neha Nirale, Vaishnavi Shete
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4410464434