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Original Article
Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques
Dr. Kamal Raj T1
Gowtham R Gowda2
Dhanush M3
Enosh Muthyala4
Gopinath S5
1 Professor and Project Guide, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: September-December 2025
Pages: 314-319
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403049
References
1. Vempati Krishna, P. Naresh, Y. David Solomon Raju, Adepu Rajesh, Ch. V. Raghavendran, “Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques,” IEEE International Conference on Intelligent Engineering and Management (ICIEM), 2022.
2. CoLeaf-DB: Coffee Leaf Nutrient Deficiency Dataset, Mendeley Data, 2023.
3. PlantVillage Dataset, Kaggle (https://www.kaggle. com/datasets/emmarex/plantdisease).
4. OpenCV Python Documentation (https://docs. opencv.org).
5. Scikit-Learn Documentation (https://scikit-learn. org).
6. J. H. Park, Y. H. Kim, and S. C. Kim, “Diagnosis of Nutrient Deficiency in Pepper Plants Using RGB Images and Machine Learning,” Journal of Sensor Science and Technology, vol. 28, no. 1, pp. 31-37,2019.
7. N. Saranya and S. Sundararajulu, “Detection of nutrient deficiency in groundnut crops using image processing techniques,” Journal of Ambient Intelli- gence and Humanized Computing, vol. 11, no. 10, pp. 4349-4357, 2020.
8. S. Sladojevic et al., “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Clas- sification,” Computational Intelligence and Neuro- science, 2016.
9. A. H. R. J. Al-Razi, A. Hasan, and M. R. Islam, “Crop disease diagnosis using machine learning algo- rithms: A review,” Journal of King Saud University- Computer and Information Sciences, vol. 34, no. 9, pp. 6363-6380, 2022.
10. Prediction of Nutrient Deficiency in Crops using Plant Health Data, 2025.
11. Prediction and detection of nutrition deficiency using machine learning, 2023.
12. Image-Based Identification of Plant Diseases and Nu- trient Deficiency Using Interpretable Machine Learning, 2024.
2. CoLeaf-DB: Coffee Leaf Nutrient Deficiency Dataset, Mendeley Data, 2023.
3. PlantVillage Dataset, Kaggle (https://www.kaggle. com/datasets/emmarex/plantdisease).
4. OpenCV Python Documentation (https://docs. opencv.org).
5. Scikit-Learn Documentation (https://scikit-learn. org).
6. J. H. Park, Y. H. Kim, and S. C. Kim, “Diagnosis of Nutrient Deficiency in Pepper Plants Using RGB Images and Machine Learning,” Journal of Sensor Science and Technology, vol. 28, no. 1, pp. 31-37,2019.
7. N. Saranya and S. Sundararajulu, “Detection of nutrient deficiency in groundnut crops using image processing techniques,” Journal of Ambient Intelli- gence and Humanized Computing, vol. 11, no. 10, pp. 4349-4357, 2020.
8. S. Sladojevic et al., “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Clas- sification,” Computational Intelligence and Neuro- science, 2016.
9. A. H. R. J. Al-Razi, A. Hasan, and M. R. Islam, “Crop disease diagnosis using machine learning algo- rithms: A review,” Journal of King Saud University- Computer and Information Sciences, vol. 34, no. 9, pp. 6363-6380, 2022.
10. Prediction of Nutrient Deficiency in Crops using Plant Health Data, 2025.
11. Prediction and detection of nutrition deficiency using machine learning, 2023.
12. Image-Based Identification of Plant Diseases and Nu- trient Deficiency Using Interpretable Machine Learning, 2024.
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