Prediction of Nutrient Concentration (PPM) in Spinach Leaves Using a YolOOD-Based Leaves Image

Alvin Saputra, Nike Dwi Grevika Drantantiyas, Ahmad Suaif, Jodes Parasian Simatupang, Naufal Danni, Ashar Hafid Nurqolby

Abstract


This study aims to develop a model for estimating nutrient content (ppm) in spinach plants using the YOLOOD architecture. Nutrient estimation is performed based on leaf image analysis as a non-destructive approach to detect nutrient deficiencies at an early stage. The method involves collecting spinach leaf images with six nutrient variation levels (100, 300, 500, 700, 900, and 1200 ppm), with 300 images per class, followed by annotation, augmentation, preprocessing, and dataset splitting into training and validation sets with a 70:30 ratio. The model is trained for 50 epochs with a batch size of 4, an input image size of 416×416 pixels, the Adam optimizer, and a learning rate of 0.0001 using default YOLOOD parameters. The model is designed to recognize visual differences in spinach leaves across nutrient levels and estimate nutrient concentration values. Performance evaluation is conducted using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the model achieves good object detection performance with an mAP@50 of 0.93; however, in the nutrient estimation stage, it obtains a MAPE of 16%, a normalized RMSE of 0.1571, and an R² of 0.7903. Therefore, the YOLOOD model is considered effective in detecting visual characteristics of spinach leaves and reasonably capable of estimating nutrient content, although further improvements are needed to enhance prediction accuracy.

Keywords


spinach; leaf image; deficiency; nutrients; YolOOD

Full Text:

PDF

References


R. H. L, “Nutrient Content of Micro/Baby-Green and Field-Grown Mature Foliage of Tropical Spinach ( Amaranthus sp.) and Roselle ( Hibiscus sabdariffa L.),” 2021.

N. Huda-Faujan, S. I. Zubairi, and A. A. A. Baker, “Nutritional and Bioactive Constituents of Antioxidant and Antimicrobial Properties in Spinacia oleracea: A Review,” Sains Malaysiana, vol. 52, no. 9, pp. 2571–2585, 2023, doi: 10.17576/JSM-2023-5209-08.

E. Sari, Mekar Zenni Radhia, and Hanifa Zaini S, “The Effect of Red Spinach Juice on Hemoglobin Levels in Pregnant Women: A Randomized Controlled Trial,” Sriwij. J. Obstet. Gynecol., vol. 2, no. 2, pp. 92–102, 2024, doi: 10.59345/sjog.v2i2.147.

M. M. Hafez, M. R. Shafeek, A. R. Mahmoud, and A. H. Ali, “Beneficial Effects of Nitrogen Fertilizer and Humic acid on Growth, Yield and Nutritive Values of Spinach (Spinacia olivera L.),” Middle East J. Appl. Sci., vol. 5, no. 2005, pp. 597–603, 2015.

C. Xu and B. Mou, “Responses of spinach to salinity and nutrient deficiency in growth, physiology, and nutritional value,” J. Am. Soc. Hortic. Sci., vol. 141, no. 1, pp. 12–21, 2016, doi: 10.21273/jashs.141.1.12.

N. Inaya, D. Armita, and H. Hafsan, “Identifikasi masalah nutrisi berbagai jenis tanaman di Desa Palajau Kabupaten Jeneponto,” Filogeni J. Mhs. Biol., vol. 1, no. 3, pp. 94–102, 2021, doi: 10.24252/filogeni.v1i3.26114.

G. P. da Silva, R. de M. Prado, and R. P. S. Ferreira, “Absorption of nutrients, growth and nutritional disorders resulting from ammonium toxicity in rice and spinach plants,” Emirates J. Food Agric., vol. 28, no. 12, pp. 882–889, 2016, doi: 10.9755/ejfa.2016-09-1294.

M. Nadafzadeh, A. Banakar, S. Abdanan, S. Minaei, A. Mounem, and G. Hoogenboom, “Smart Agricultural Technology Identification of nutrient deficiency stress for iron , zinc and manganese in baby spinach using computer vision,” Smart Agric. Technol., vol. 12, no. May, p. 101524, 2025, doi: 10.1016/j.atech.2025.101524.

M. Nadafzadeh, A. Banakar, S. A. Mehdizadeh, S. Minaei, A. M. Mouazen, and G. Hoogenboom, “Identification of nutrient deficiency stress for iron, zinc and manganese in baby spinach using computer vision,” Smart Agric. Technol., vol. 12, no. May, p. 101524, 2025, doi: 10.1016/j.atech.2025.101524.

M. F. Taha et al., “Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics,” Chemosensors, vol. 10, no. 2, pp. 1–23, 2022, doi: 10.3390/chemosensors10020045.

A. Zolfi et al., “YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 5788–5797, 2024, doi: 10.1109/CVPR52733.2024.00553.

E. Tsoumalakou, E. Mente, and N. Vlahos, “Spinach Responds to Minimal Nutrient Supplementation in Aquaponics by Up-Regulating Light Use Efficiency , Photochemistry , and Carboxylation,” 2023.

C. Isaza et al., “Spinach ( Spinacia oleracea L .) Growth Model in Indoor Controlled Environment Using Agriculture 4 . 0,” pp. 1–24, 2025.

S. Raschka, “Model Evaluation , Model Selection , and Algorithm Selection in Machine Learning arXiv : 1811 . 12808v3 [ cs . LG ] 11 Nov 2020,” 2018.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.




DOI: https://doi.org/10.31284/j.iptek.2026.v30i1.9031

Refbacks

  • There are currently no refbacks.


Indexed by:
SINTA logo Google Scholar logo Dimensions logo GARUDA logo Crossref logo Worldcat logo Base logo Scilit logo