Original Research Article

Article volume = 2023 and issue = 2

Pages: 171–176

Article publication Date: November 28, 2023

You can download PDF file of the article here: Download

Visited 219 times and downloaded 122 times

Identification of Nutrient Deficiency and Disease Detection in Medicinal Plants: A Review

Shilpa K.C.(a) and Nirmala C.R.(b)

(a) Research Scholar, Department of Computer Science & Engg., Bapuji Institute of Engineering & Technology, Davanagere-577004, India.

(b) Department of Computer Science & Engg., Bapuji Institute of Engineering & Technology, Davanagere-577004, India.

Abstract:

Medicinal plants are an essential source of medicinal chemicals used in both conventional and alternative medicine. The pharmaceutical and healthcare sectors must guarantee their optimum development and well-being. In this study, we explore the potentially useful application of one-shot learning approaches for the diagnosis of illnesses and nutrient deficiencies in medicinal plants, offering a practical and economical approach to plant health management.

Keywords:

Medicinal plants, nutrient deficiencies, one-shot learning approach.

References:
  • [1] Aleksandrov, V. (2019). Identification of nutrient deficiency in bean plants by prompt chlorophyll fluorescence measurements and Artificial Neural Networks. arXiv preprint arXiv:1906.03312. 1.1
  • [2] Leena, N., & Saju, K. K. (2018, April). Vision based nutrient deficiency classification in maize plants using multi class support vector machines. In AIP Conference Proceedings (Vol. 1952, No. 1). AIP Publishing. 1.1
  • [3] Vassallo-Barco, M., Vives-Garnique, L., Tuesta-Monteza, V.,Mejía-Cabrera, H. I., & Toledo, R. Y. (2017). Automatic Detection of Nutritional Deficiencies in Coffee Tree Leaves through Shape and Texture Descriptors. Journal of Digital Information Management, 15(1). 1.1
  • [4] Lavanya, M., Devi, M. K., & Vani, M. S. (2022). Deep Learning for Identification of Plant nutrient Deficiencies. Journal of Pharmaceutical Negative Results, 3284-3291. 2
  • [5] Waheed, H., Zafar, N., Akram, W., Manzoor, A., Gani, A., & Islam, S. U. (2022). Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for Zingiberaceae Crop. Agriculture, 12(6), 742. 2
  • [6] Ponce, H., Cevallos, C., Espinosa, R., & Gutiérrez, S. (2021). Estimation of low nutrients in tomato crops through the analysis of leaf images using machine learning. Journal of Artificial Intelligence and Technology, 1(2), 131-137. 2
  • [7] Jose, A., Nandagopalan, S., Ubalanka, V., & Viswanath, D. (2021, May). Detection and classification of nutrient deficiencies in plants using machine learning. In Journal of Physics: Conference Series (Vol. 1850, No. 1, p. 012050). IOP Publishing. 2
  • [8] Sathyavani, R., JaganMohan, K., & Kalaavathi, B. (2021). Detection of plant leaf nutrients using convolutional neural network-based internet of things data acquisition. International Journal of Nonlinear Analysis and Applications, 12(2), 1175-1186. 2
  • [9] Amirtha, T., Gokulalakshmi, T., Umamaheswari, P., & Tech, T. R. M. (2020).Machine Learning Based Nutrient Deficiency Detection in Crops. Int. J. Recent Technol. Eng, 8(6), 5330-5333. 2
  • [10] Tenorio, F. A., Eagle, A. J., McLellan, E. L., Cassman, K. G., Howard, R., Below, F. E. & Grassini, P. (2019). Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region. Field Crops Research, 240, 185-193. 2
  • [11] Wulandhari, L. A., Gunawan, A. A. S., Qurania, A., Harsani, P., Tarawan, T. F., & Hermawan, R. F. (2019). Plant nutrient deficiency detection using deep convolutional neural network. ICIC Express Lett, 13(10), 971-977. 2
  • [12] Li, Y., & Chao, X. (2021). Semi-supervised few-shot learning approach for plant diseases recognition. PlantMethods, 17, 1-10. 2.1
  • [13] Zhong, F., Chen, Z., Zhang, Y., & Xia, F. (2020). Zero-and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders. Computers and Electronics in Agriculture, 179, 105828. 2.1
  • [14] Argüeso, D., Picon, A., Irusta, U., Medela, A., San-Emeterio, M. G., Bereciartua, A., & Alvarez-Gila, A. (2020). Few-Shot Learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture, 175, 105542. 2.1
Cite this article as:
  • Shilpa K.C. and Nirmala C.R., Identification of Nutrient Deficiency and Disease Detection in Medicinal Plants: A Review, Communications in Combinatorics, Cryptography & Computer Science, 2023(2), PP.171–176, 2023
  • Export citation to BibTeX