Cultivating Success using CNN-Powered Precision Farming

Convolutional Neural Networks (CNNs), a machine learning model, are included into a mobile application created using Flutter to offer a comprehensive analysis of plant recommendations using soil prediction for soil types in India. Using CNNs, users may take their own photographs or select ones from the gallery to analyse in order to accurately categorise soil. Based on the identified kind of soil, the application suggests plants that are suitable for cultivation, assisting farmers and gardeners in making informed selections. This research study covers the significance of soil analysis and plant selection in agriculture in detail. The methodology part covers the construction of the Flutter application as well as the implementation of the CNN model. The trial results highlight the system’s potential impact on agricultural productivity and sustainability while also demonstrating the system’s effectiveness in categorising soil and proposing plants. The study discusses the ramifications of the research findings as well as possible future prospects for computer vision and agriculture research.