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All Journal HAYATI Journal of Biosciences Jurnal Keteknikan Pertanian Tropis dan Biosistem
Suharto, Yohanes Bayu
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Agriculture, Biological Sciences & Forestry Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Mechanical Engineering

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Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse Suharto, Yohanes Bayu; Suhardiyanto, Herry; Susila, Anas Dinurroman; Supriyanto
HAYATI Journal of Biosciences Vol. 32 No. 2 (2025): March 2025
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.32.2.300-309

Abstract

Photosynthesis is one of the essential processes in plant physiology that produces glucose and oxygen to support plant growth. Nutrient stress conditions will affect the photosynthetic rate in plants. The model predicting photosynthetic rates based on environmental conditions, nutrients, and plant types will be highly beneficial for farmers in tweaking these variables to maximize plant photosynthesis. This research focused on assessing the impact of nutrient stress on the photosynthetic rate in leaf vegetable crops and aimed to create a model using artificial neural networks (ANN) to predict photosynthetic rates under nutrient-stress conditions. Leaf vegetable crops were cultivated in a greenhouse using the NFT hydroponic system with eight nutrient conditions. This paper introduces an ANN model featuring nine input variables, ten hidden layers, and a single output. This model aims to elucidate the relationship between these inputs and the output parameter. The statistical analysis revealed a notable disparity in the CO2 assimilation rate among leaf vegetable crops subjected to nutrient stress treatment. The constructed ANN model demonstrated strong performance, achieving an R2 value of 0.9416, an RMSE of 1.5898 during training, and an R2 value of 0.9271 with an RMSE of 1.9649 in validation. A combination of statistical analysis and ANN modeling accurately explained the relationship and influence of input parameters, especially nutrient stress conditions, on the photosynthetic rate of leaf vegetable plants cultivated hydroponically in a greenhouse.
Correlation Analysis of RGB Image-Based Vegetation Indices to Chlorophyll Content in Leafy Vegetables with Nutrient-Stressed Condition: Analisis Korelasi Indeks Vegetasi Berbasis Citra RGB Terhadap Kandungan Klorofil pada Tanaman Sayuran Daun dengan Kondisi Cekaman Hara Suharto, Yohanes Bayu; Suhardiyanto, Herry; Messaline, Theressa
Journal of Tropical Agricultural Engineering and Biosystems - Jurnal Keteknikan Pertanian Tropis dan Biosistem Vol. 13 No. 2 (2025): August 2025
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jkptb.2025.013.02.03

Abstract

Nutrient stress is one of the main factors affecting the growth and productivity of leafy vegetables. Chlorophyll content is often used to indicate plant nutrient status, but conventional measurement methods are destructive and inefficient. This study aims to analyze the correlation between various RGB camera image-based vegetation indices and chlorophyll content in hydroponically cultivated leafy vegetables under nutrient-stress treatment. The six vegetation indices used in this study are Excess Green (EXG), Visible-band Difference Vegetation Index (VDVI), RGB Vegetation Index (RGBVI), Normalized Green Blue Difference Index (NGBDI), Green-Red Vegetation Index (GRVI), and Visible Atmospherically Resistant Index (VARI). RGB image data were captured using an RGB digital web camera sensor (Xiaovv XVV-6320S) on a photo box set under controlled lighting conditions. At the same time, chlorophyll content was measured using a SPAD-502 Chlorophyll Meter. Pearson correlation analysis showed that the vegetation indices VARI (r = 0.90, R² = 0.82) and GRVI (r = 0.89, R² = 0.80) had robust correlations with chlorophyll content, making them the best indices for RGB image-based estimation of chlorophyll content in leafy vegetables. The results of this study indicate that RGB image-based vegetation indices can be an efficient, non-destructive method for detecting nutrient stress in leafy vegetables and have the potential to be applied in precision agriculture systems and automated monitoring in greenhouses.
Co-Authors Herry Suhardiyanto Jayawarsa, A.A. Ketut Messaline, Theressa Susila, Anas Dinurroman