Plant Disease Severity Detection and Fertilizer Recommendation using Deep Learning Techniques. Sudhir, B., Teja, D., C., Sai, K., Sridhar, P., & Daniya, T. Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023. ![Plant Disease Severity Detection and Fertilizer Recommendation using Deep Learning Techniques [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex In India, the agriculture industry plays a significant role in the economy and employs a sizable section of the workforce. The demand for food is increasing and analysis of agriculture data can help improve practices and increase productivity by providing insights into crop diseases and weather conditions. Plant diseases can greatly impact agricultural productivity, and early detection is crucial to avoiding losses. The proposed project makes use of different ML techniques such as KNN, SVM, and DL techniques such as CNN and ANN to detect plant diseases in an efficient and effective manner. These techniques can be trained on large datasets to learn patterns and make predictions, making them well suited for this task. The Deep Learning system includes a system that automatically scans leaf images and detects disease based on visual symptoms. This system also calculates severity level of disease and suggests suitable amount of fertilizer for disease to soak in their crop according to severity level. A user interface was created to help farmers and agriculture workers for easy usage by simple capturing leaf image and get suggestions, this helps farmers to increase their crop production and to maintain quality of crop.
@article{
title = {Plant Disease Severity Detection and Fertilizer Recommendation using Deep Learning Techniques},
type = {article},
year = {2023},
keywords = {ANN,CNN,Deep Learning,KNN,Machine Learning,Prediction,SVM,Severity},
pages = {215-221},
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abstract = {In India, the agriculture industry plays a significant role in the economy and employs a sizable section of the workforce. The demand for food is increasing and analysis of agriculture data can help improve practices and increase productivity by providing insights into crop diseases and weather conditions. Plant diseases can greatly impact agricultural productivity, and early detection is crucial to avoiding losses. The proposed project makes use of different ML techniques such as KNN, SVM, and DL techniques such as CNN and ANN to detect plant diseases in an efficient and effective manner. These techniques can be trained on large datasets to learn patterns and make predictions, making them well suited for this task. The Deep Learning system includes a system that automatically scans leaf images and detects disease based on visual symptoms. This system also calculates severity level of disease and suggests suitable amount of fertilizer for disease to soak in their crop according to severity level. A user interface was created to help farmers and agriculture workers for easy usage by simple capturing leaf image and get suggestions, this helps farmers to increase their crop production and to maintain quality of crop.},
bibtype = {article},
author = {Sudhir, Buddepu and Teja, Devalaraju Charan and Sai, Kurra and Sridhar, Peddinti and Daniya, T.},
doi = {10.1109/ICAAIC56838.2023.10140467},
journal = {Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023}
}
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