A Precision Agriculture Framework Using DoubleGAN and Machine Learning for Grape Leaf Irrigation Scheduling and Yield Prediction
Journal of Smart and Sustainable Farming
Received On : 28 May 2025
Revised On : 16 July 2025
Accepted On : 30 August 2025
Published On : 02 September 2025
Volume 01, 2025
Pages : 150-158
Abstract
We propose an integrated Double Generative Adversarial Networks (DoubleGAN) machine learning framework used to improve the irrigation timing of grapevines and disease classification, as well as predict yields. High-resolution grape leaf images were enhanced by DoubleGAN to eliminate class imbalance because field data, such as irradiance, soil pH, crop type, rainfall, temperature, and humidity, were normalized. We tested irrigation scheduling using Naive Bayes, Random Forest, and Decision Tree models, with the latter achieving the highest accuracy at 75%. In the detection of the disease, both VGG16 and ResNet50 achieved better performances using DoubleGAN-augmented datasets, achieving an accuracy of 99.39%. Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Boosted Regression Tree (BRT) participated in the prediction of crop yield, and BRT had minimum errors. Findings affirm that DoubleGAN enhances model robustness, allowing accurate data-driven decision support in viticulture.
Keywords
DoubleGAN, Machine Learning, Irrigation Scheduling, Grape Leaves, Disease Detection, Yield Prediction, Deep Learning, Precision Agriculture.
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Cite this Article
Jing Yue, “A Precision Agriculture Framework Using DoubleGAN and Machine Learning for Grape Leaf Irrigation Scheduling and Yield Prediction”, Journal of Smart and Sustainable Farming, pp. 150-158, 2025, doi: 10.64026/JSSF/2025015.