ENHANCING CROP HEALTH THROUGH DIGITAL TWIN FOR DISEASE MONITORING AND NUTRIENT BALANCE

Sobhana Mummaneni


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0001-5938-5740

Tribhuvana Sree Sappa

tribhuvanasree@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0009-8594-429X

Venkata Gayathri Devi Katakam


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0002-3492-4386

Abstract

Digital Twins is a digital replica of a physical object to observe its real-time performance, gather data, and recommend corrective actions if required to enhance its performance. This fascinating technological idea is now reaching the agriculture fields to transform farming, by creating digital twins of entire farms. This initiative presents an innovative strategy to enhance crop health and yield by creating a digital twin for paddy fields. The aim is to enable early detection of nutrient deficiencies and leaf blast disease, leading to a transformation in agriculture. Creating virtual replicas of plants and fields, the digital twin harnesses real-time data and advanced analytics to transform the way agricultural systems are managed. By integrating remote sensing, data analytics, and various Internet of Things devices like pH, nitrous, potassium, and phosphorus sensors, coupled with a gateway system, the digital twin provides real-time monitoring and analysis of crop health and nutrient levels. Employing advanced machine learning algorithms, notably Convolutional Neural Networks ensures precise and early detection of nutrient deficiencies and crop diseases. This ground-breaking technology provides timely alerts and actionable insights to farmers, enabling proactive decision-making for optimal crop management. This farmland digital twin represents a transformative approach towards agricultural sustainability and enhancing productivity.


Keywords:

agricultural sustainability, convolution neural networks, digital twin, internet of things, nutrient deficiency detection

Abbas A. et al.: Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture. Agronomy 13(6), 2023, 1524.
  Google Scholar

Asibi A. E., Chai Q., Coulter J. A.: Rice blast: A disease with implications for global food security. Agronomy 9(8), 2019, 451.
  Google Scholar

Awan J.: Digital Twins for Agriculture - Blog Des Fraunhofer IESE. Fraunhofer IESE, 25 Nov. 2020 [www.iese.fraunhofer.de/blog/digital-twins-agriculture] (avaible 29.09.2023).
  Google Scholar

Bastiaans L.: Effects of leaf blast on growth and production of a rice crop: 1. Determining the mechanism of yield reduction. Netherlands Journal of Plant Pathology 99, 1993, 323–334.
  Google Scholar

Blast (Leaf and Collar), IRRI Rice Knowledge Bank. [www.knowledgebank.irri.org,www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/blast-leaf-collar] (avaible 29.09.2023).
  Google Scholar

Bravo C. et al.: Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84(2), 2003, 137–145.
  Google Scholar

Chaux J. D. et al.: A digital twin architecture to optimize productivity within controlled environment agriculture. Applied Sciences 11(19), 2021, 8875.
  Google Scholar

Jose A. et al.: Detection and classification of nutrient deficiencies in plants using machine learning. Journal of Physics: Conference Series 1850(1), 2021.
  Google Scholar

Kalaji H. M. et al.: Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants. Photosynthesis Research 136, 2018, 329–343.
  Google Scholar

Latte M. V., Shidnal S., Anami B. S.: Rule based approach to determine nutrient deficiency in paddy leaf images. International Journal of Agricultural Technology 13(2), 2017, 227–245.
  Google Scholar

Lau H. Y., Botella J. R.: Advanced DNA-based point-of-care diagnostic methods for plant diseases detection. Frontiers in plant science 8, 2017.
  Google Scholar

Nayak A. et al.: Application of smartphone-image processing and transfer learning for rice disease and nutrient deficiency detection. Smart Agricultural Technology 4, 2023, 100195.
  Google Scholar

Nutrient-Deficiency-Symptoms-In-Rice. [www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice] (avaible 27.09.2023).
  Google Scholar

Paiman J. et al.: Maximizing the Rice Yield (Oryza Sativa L.) Using NPK Fertilizer. The Open Agriculture Journal 15(1), 2021, 33–38, [https://doi.org/10.2174/1874331502115010033].
  Google Scholar

Peladarinos N. et al.: Enhancing smart agriculture by implementing digital twins: A comprehensive review. Sensors 23(16), 2023, 7128.
  Google Scholar

Rice Blast, Rice, Agriculture: Pest Management Guidelines. UC Statewide IPM Program (UC IPM) [ipm.ucanr.edu/agriculture/rice/rice-blast] (avaible 29.09.2023).
  Google Scholar

Rice Diseases Image Dataset [www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset] (avaible 29.09.2023).
  Google Scholar

Rice Production by Country. World Agricultural Production 2023/2024 [www.worldagriculturalproduction.com/crops/rice.aspx] (avaible 29.09.2023).
  Google Scholar

Shivappa R. et al.: Emerging minor diseases of rice in India: losses and management strategies. Integrative Advances in Rice Research, 2021.
  Google Scholar

Talukder Md S. H. et al.: An Improved Model for Nutrient Deficiency Diagnosis of Rice Plant by Ensemble Learning. 4th International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, 2022.
  Google Scholar

Terentev A., Dolzhenko V.: Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review. Sensors 23(12), 2023, 5366.
  Google Scholar

Wang C. et al.: Classification of nutrient deficiency in rice based on CNN model with Reinforcement Learning augmentation. International Symposium on Artificial Intelligence and its Application on Media (ISAIAM). IEEE, 2021.
  Google Scholar

Xu Z. et al.: Using deep convolutional neural networks for image-based diagnosis of nutrient deficiencies in rice. Computational Intelligence and Neuroscience, 2020, 7307252.
  Google Scholar

Download


Published
2024-03-31

Cited by

Mummaneni, S., Sappa, T. S., & Katakam, V. G. D. (2024). ENHANCING CROP HEALTH THROUGH DIGITAL TWIN FOR DISEASE MONITORING AND NUTRIENT BALANCE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(1), 57–62. https://doi.org/10.35784/iapgos.5626

Authors

Sobhana Mummaneni 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0001-5938-5740

Authors

Tribhuvana Sree Sappa 
tribhuvanasree@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0009-8594-429X

Authors

Venkata Gayathri Devi Katakam 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0002-3492-4386

Statistics

Abstract views: 58
PDF downloads: 50


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Most read articles by the same author(s)