Fall 2023 Capstone Project: Aquaponic Deep Water Systems

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Summary

This semester, our team worked on an image classification problem involving plants grown aquaponically. Our collaborators at Pure Reason AI are currently working on growing plants in a deep-water culture system here at LSU. This system works by transferring nutrients produced by fish to the plants that are grown using only water. This method takes away the need for soil and produces plants that only need water and nutrients to grow. Our goal this semester was to classify images of lettuce plants into one of four categories: fully nutritious (FN), potassium deficient (K), nitrogen deficient (N), or phosphorus deficient (P). We accomplished this goal by training two neural networks to detect the deficiencies in the images and classify them accordingly. This goal was met by researching similar projects, utilizing Python to create the neural networks, and training them to classify images on a dataset provided by our collaborators. Some future works for this project include researching more methods for image classification and developing our neural networks to handle issues such as light, shadows, and noise. Our collaborators are interested in classifying images based on light and possibly applying Spectral Theory to detect the deficiencies. We accomplished our goal of classifying images based on nutrient deficiencies, but more work is needed to help create a user-friendly system that can be used by anyone growing plants aquaponically.

Images of lettuce with various nutrient deficiencies Diagram of an aquaponic deep-water culture system

References

Aquaponic Systems

Deep Water Culture Systems

Economic Data

Economic Analysis

Nutrient Deficiency Detection

  • Eshkabilov, Sulaymon, Arim Lee, Xin Sun, Chiwon W. Lee, and Halis Simsek. “Hyperspectral Imaging Techniques for Rapid Detection of Nutrient Content of Hydroponically Grown Lettuce Cultivars.” Computers and Electronics in Agriculture 181 (February 1, 2021): 105968. https://doi.org/10.1016/j.compag.2020.105968.
  • Mani, Balamurugan, and Jayalakshmi Shanmugam. “Estimating Plant Macronutrients Using VNIR Spectroradiometry.” Polish Journal of Environmental Studies 28, no. 3 (February 18, 2019): 1831–37. https://doi.org/10.15244/pjoes/89585.
  • Pacumbaba, R. O., and C. A. Beyl. “Changes in Hyperspectral Reflectance Signatures of Lettuce Leaves in Response to Macronutrient Deficiencies.” Advances in Space Research 48, no. 1 (July 1, 2011): 32–42. https://doi.org/10.1016/j.asr.2011.02.020.
  • Petrazzini, Lauro L., Guilherme A. Souza, Cléber L. Rodas, Eduardo B. Emrich, Janice G. Carvalho, and Rovilson J. Souza. “Nutritional Deficiency in Crisphead Lettuce Grown in Hydroponics.” Horticultura Brasileira 32 (September 2014): 310–13. https://doi.org/10.1590/S0102-05362014000300012.

Using Convolutional Neural Networks for Nutrient Deficiency Detection

  • Gui, Z; Bora, S. “Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil”, Sensors Volume 23, Issue no. 12 (June 2023): 5407. https://doi.org/10.3390/s23125407
  • Taha, M; Abdalla, A; ElMasry, G; Gouda, M; Zhou, L; Zhao, N; Liang, N; Ziu, Z; Hassanein, A; Al-Rejaie, S; He, Y; Qiu, Z. “Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics” Chemosensors 10, no. 2 (January 2022): 45. https://doi.org/10.3390/chemosensors10020045
  • Yudha Pratama, Ichsan, Abdi Wahab, and Mudrik Alaydrus. “Deep Learning for Assessing Unhealthy Lettuce Hydroponic Using Convolutional Neural Network Based on Faster R-CNN with Inception V2.” In 2020 Fifth International Conference on Informatics and Computing (ICIC), 1–6, 2020. https://doi.org/10.1109/ICIC50835.2020.9288554.

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