Local Chapter Oyo, Nigeria Chapter
Coordinated byNigeria ,
Status: Completed
Project Duration: 08 Mar 2023 - 18 Apr 2023
Open Source resources available from this project
Project background.
The growth of plants and other terrestrial species depends heavily on the soil, which is a crucial component of our planet. Plant health and productivity are influenced by the soil’s quality. To increase crop output and reduce soil deterioration, it is important in agriculture to understand the qualities of the soil. In the past, approaches for soil analysis have depended on labor- and time-intensive manual procedures that frequently yield unreliable results.
Recent developments in deep learning have made it possible to analyze and detect dirt in new ways. Artificial neural networks are used in deep learning, a type of machine learning, to process and evaluate massive volumes of data. Deep learning allows for the training of models that can recognize and classify various soil types automatically based on their physical and chemical characteristics. This technique has the potential to completely transform soil analysis, making it quicker, more precise, and less expensive.
Despite these trends, deep learning applications for soil analysis and detection remain underestimated. Through the creation of a deep learning model for soil fertility detection and analysis, the proposed project seeks to address this issue. Large datasets of soil features and qualities will be used in the project to train the model, which will then be applied to automatically identify various soil types based on their physical, chemical characteristics and fertility. The results of this study will help to develop soil analysis techniques that are more effective and efficient, which will have a big impact on agricultural and environmental science.
The problem.
Agriculture depends heavily on soil analysis and detection, but the present approaches are frequently labor, time intensive and yield variable results. This problem created a need for more effective and efficient soil analysis techniques that can deliver precise and quick information on the characteristics and quality of the soil.
Traditional soil analysis techniques rely on labor-intensive manual processes that can produce variable results. Additionally, their broad use is constrained by the fact that they can be a lot and frequently require expensive equipment and educated employees. By offering a faster and more precise approach to soil analysis, deep learning has the potential to revolutionize soil fertility identification and analysis.
An artificial intelligence solution can help identify if the soil is fertile or not for agricultural purposes.
Project goals.
Project plan.
Week 1
Data Collection and Exploratory Data Analysis
Week 2
Preprocessing and Feature Extraction
Week 3
Model Development, training and Evaluation
Week 4
App development
Learning outcomes.
– Soil Image Processing
– Computer vision
– Soil Image Analysis
– Project management
