Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection
Abstract & Details
Research Area
Computer Engineering
Keywords
Crop Selection
Fertilizer Optimization
Leaf Disease Management
Machine Learning
Data Analysis
Precision Agriculture
Soil Fertility Decline
Convolutional Neural Networks (CNNs)
Random Forest
ResNet-50 Model
Environmental Impact on Agriculture.
Abstract
Agriculture is a cornerstone of the global economy, playing a crucial role in GDP and employment. As the global population continues to rise, enhancing agricultural productivity is essential to ensure food security. Crop yield is influenced by a range of factors including temperature, rainfall, soil conditions, and fertilizer application, leading to fluctuations in production. Over the years, soil fertility has been declining due to environmental factors, making it increasingly challenging to maintain high crop yields. This research introduces a web application that assists farmers in making informed decisions regarding crop selection, fertilizer application, and leaf disease management. By harnessing advanced data analysis and machine learning techniques, the platform provides personalized recommendations based on soil quality, climate, and geographical data. Machine learning algorithms are used for crop and fertilizer suggestions, while deep learning models like CNN ResNet-50 are employed for leaf disease detection. The system aims to enhance yield and sustainability, thereby improving agricultural productivity and ensuring global food security. By optimizing resource use and reducing crop losses due to diseases, this solution addresses the challenges posed by declining soil fertility and promotes sustainable farming practices.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Rani Ishwarashetti | CMR University Bangalore |
| 2 | Syeeda Mujeebunnisa | CMR University Bangalore |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Ishwarashetti, Rani & Mujeebunnisa, Syeeda (2024). Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection. International Journal of Advance Research and Innovative Ideas In Education, 10(5), 605-617.
MLA Style
Ishwarashetti, Rani, and Syeeda Mujeebunnisa. "Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, 2024, pp. 605-617.
IEEE Style
Rani Ishwarashetti and Syeeda Mujeebunnisa, "Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, pp. 605-617, 2024.
Vancouver Style
Ishwarashetti Rani, Mujeebunnisa Syeeda. Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(5):605-617.
Harvard Style
Ishwarashetti, Rani & Mujeebunnisa, Syeeda (2024) 'Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection', International Journal of Advance Research and Innovative Ideas In Education, 10(5), pp. 605-617.
Chicago Style
Ishwarashetti, Rani and Syeeda Mujeebunnisa. "Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 605-617.
Turabian Style
Ishwarashetti, Rani and Syeeda Mujeebunnisa. "Agroadvisor: Empowering Farmers with Data- Driven Crop, Fertilizer Recommendation, And Leaf Disease Detection." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 605-617.
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