An Innovative AI-Driven & Cloud Based Platform for Farmers Enabling Plant Disease Identification, Tracking and Forecasting

V Keerthana; A Sai Priya; H Muskan; E Tejesh Kumar; B Ramesh; B Pradeepthi; S Prakash

doi:https://doi.org/10.47001/JAIET/2025.204002

Authors

V Keerthana

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

A Sai Priya

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

H Muskan

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

E Tejesh Kumar

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

B Ramesh

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

B Pradeepthi

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Prakash

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

It highlights the traits and prerequisites as well as the challenges associated with the identification and understanding of plant diseases. It demonstrates how convolutional neural networks (CNN) are used to classify and verify diseases affecting crops. A plant disease diagnosis system is introduced in the document. Using convolutional neural networks, our detection and identification system precisely determines the type and severity of plant diseases. In this AI-driven project, we create a deep neural network model that categorizes plant diseases in images into various groups. With the help of this model, farmers can instantly diagnose diseases via a mobile app, accessing expert insights and preventive strategies. A geo-tagged database enables disease density mapping and spread forecasting, empowering proactive crop management. Experts analyze spatial disease trends through an interactive web platform. This scalable, cloud-based service revolutionizes agricultural disease management, reducing pesticide dependency and fostering sustainable crop production.

Keywords

Plant Disease Diagnosis AI-driven Solution Convolutional Neural Network (CNN) Deep Neural Network Model Image Processing Disease Classification Mobile App for Farmers Cloud-Based Platform Geo-Tagged Data Disease Density Mapping Spread Forecasting Expert Interaction Sustainable Agriculture Pesticide Reduction Scalable Agricultural Solution

Citation of this Article

V Keerthana, A Sai Priya, H Muskan, E Tejesh Kumar, B Ramesh, B Pradeepthi, & S Prakash. (2025). An Innovative AI-Driven & Cloud Based Platform for Farmers Enabling Plant Disease Identification, Tracking and Forecasting. Journal of Artificial Intelligence and Emerging Technologies. 2(4), 5-9. Article DOI: https://doi.org/10.47001/JAIET/2025.204002

References

L. Saxena and L. Armstrong, “A survey of image processing techniques for agriculture,” in Proceedings of Asian Federation for Information Technology in Agriculture, 2014, pp. 401-413.
E. L. Stewart and B. A. McDonald, “Measuring quantitative virulence in the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis,” in Phytopathology 104 9, 2014, pp. 985– 992.
A.Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012.
TensorFlow.[Online].Available: https://www.tensorflow.org/
D. P. Hughes and M. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowd sourcing,” in CoRR abs/1511.08060, 2015.
M. Sardogan, A. Tuncer and Y. Ozen, "Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm," 2018 3rd International Conference on Computer Science and Engineering (UBMK), 2018, pp. 382-385, doi: 10.1109/UBMK.2018.8566635.
E. Hossain, M. F. Hossain and M. A. Rahaman, "A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier," 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1-6, doi: 10.1109/ECACE.2019.8679247.
Saleem, M.H.; Potgieter, J.; Arif, K.M. Plant Disease Detection and Classification by Deep Learning. Plants 2019, 8, 468. https://doi.org/10.3390/plants8110468.
Wang, H.; Shang, S.; Wang, D.; He, X.; Feng, K.; Zhu, H. Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model. Agriculture 2022, 12, 931. https://doi.org/10.3390/agriculture12070931.
M. Francis and C. Deisy, "Disease Detection and Classification in Agricultural Plants Using Convolutional Neural Networks — A Visual Understanding," 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019, pp. 1063-1068, doi: 10.1109/SPIN.2019.8711701.
U. Shruthi, V. Nagaveni and B. K. Raghavendra, "A Review on Machine Learning Classification Techniques for Plant Disease Detection," 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 281-284, doi: 10.1109/ICACCS.2019.8728415.
Dhingra, G., Kumar, V. & Joshi, H.D. Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77, 19951–20000 (2018). https://doi.org/10.1007/s11042-017-5445-8
K. Elangovan and S. Nalini, “Plant disease classification using image segmentation and SVM techniques”, International Journal of Computational Intelligence Research, vol. 13(7), pp. 1821-1828, 2017.
M. M. Ozguven, K. Adem, Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms, Physica A: Statistical Mechanics and its Applications, Volume 535, 2019, 122537, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2019.122537.
W. M. Ead and M. M. Abbassy, "IOT Based on Plant Diseases Detection and Classification," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 2030-2033, doi: 10.1109/ICACCS51430.2021.9441954.

[1] L. Saxena and L. Armstrong, “A survey of image processing techniques for agriculture,” in Proceedings of Asian Federation for Information Technology in Agriculture, 2014, pp. 401-413.

[2] E. L. Stewart and B. A. McDonald, “Measuring quantitative virulence in the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis,” in Phytopathology 104 9, 2014, pp. 985– 992.

[3] A.Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012.

[4] TensorFlow.[Online].Available: https://www.tensorflow.org/

[5] D. P. Hughes and M. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowd sourcing,” in CoRR abs/1511.08060, 2015.

[6] M. Sardogan, A. Tuncer and Y. Ozen, "Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm," 2018 3rd International Conference on Computer Science and Engineering (UBMK), 2018, pp. 382-385, doi: 10.1109/UBMK.2018.8566635.

[7] E. Hossain, M. F. Hossain and M. A. Rahaman, "A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier," 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1-6, doi: 10.1109/ECACE.2019.8679247.

[8] Saleem, M.H.; Potgieter, J.; Arif, K.M. Plant Disease Detection and Classification by Deep Learning. Plants 2019, 8, 468. https://doi.org/10.3390/plants8110468.

[9] Wang, H.; Shang, S.; Wang, D.; He, X.; Feng, K.; Zhu, H. Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model. Agriculture 2022, 12, 931. https://doi.org/10.3390/agriculture12070931.

[10] M. Francis and C. Deisy, "Disease Detection and Classification in Agricultural Plants Using Convolutional Neural Networks — A Visual Understanding," 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019, pp. 1063-1068, doi: 10.1109/SPIN.2019.8711701.

[11] U. Shruthi, V. Nagaveni and B. K. Raghavendra, "A Review on Machine Learning Classification Techniques for Plant Disease Detection," 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 281-284, doi: 10.1109/ICACCS.2019.8728415.

[12] Dhingra, G., Kumar, V. & Joshi, H.D. Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77, 19951–20000 (2018). https://doi.org/10.1007/s11042-017-5445-8

[13] K. Elangovan and S. Nalini, “Plant disease classification using image segmentation and SVM techniques”, International Journal of Computational Intelligence Research, vol. 13(7), pp. 1821-1828, 2017.

[14] M. M. Ozguven, K. Adem, Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms, Physica A: Statistical Mechanics and its Applications, Volume 535, 2019, 122537, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2019.122537.

[15] W. M. Ead and M. M. Abbassy, "IOT Based on Plant Diseases Detection and Classification," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 2030-2033, doi: 10.1109/ICACCS51430.2021.9441954.