All Issue

2025 Vol.7, Issue 1

Research Article

Cucumber fruit weight prediction using deep learning models on Korea's smart agriculture big data platform
Im-joung Choi1
,
Shahriar Ahmed2
,
Sun-Ok Chung23
,
Myongkyoon Yang4*
1Korea Smart Farm R&D Foundation, Sejong 30121, Republic of Korea
2Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
3Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
4Department of Bioindustrial Machinery Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
*Corresponding Author
31 March 2025. pp. 1-16
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Abstract

The integration of artificial intelligence (AI) in smart agriculture has enabled precise crop monitoring and yield prediction, contributing to improved resource management and productivity. However, integrating multiple data types to build an AI model for crop monitoring and yield could be challenging. This study develops and evaluates deep learning models for predicting cucumber fruit weight by utilizing structured time-series data from a smart farm database. The dataset, collected from a protected horticulture environment, incorporates key growth parameters (leaf length, leaf count, and leaf width) and environmental factors (internal temperature, relative humidity, and solar radiation). Three deep learning models—Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Network (1D-CNN), and Transformer—were implemented to analyze the relationships between these factors and fruit weight. Several preprocessing steps, including anomaly handling and data purification, were applied to improve data quality and model performance. Complex variables reflecting the interaction between growth and environmental factors were generated, and ideal values were adjusted to minimize distortion without data loss. The models were trained to predict cucumber harvest overload using 10-fold cross-validation to avoid overfitting. The results indicate that all models demonstrated strong predictive capabilities, with the Transformer model achieving the highest accuracy (MAE = 0.1584, R2 = 0.9999), followed by GRU (MAE = 3.5735, R2 = 0.9728) and 1D-CNN (MAE = 3.8413, R2 = 0.9733). The superior performance of the Transformer model is attributed to its ability to capture complex dependencies within time-series data. GRU effectively modeled sequential dependencies, while 1D-CNN efficiently extracted local features from temporal sequences. This study highlights the potential of deep learning models in smart agriculture for growth monitoring and precision farming. However, the research is limited by the dataset’s geographic scope and the exclusion of additional agronomic variables such as soil conditions and nutrient levels. Future research should explore model optimization, diverse environmental datasets, and real-time deployment in smart farming systems.

Keywords
Smart agriculture
Big-data platform
Artificial intelligence
Deep learning
Time-series model
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Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 7
  • No :1
  • Pages :1-16
  • Received Date : 2025-03-10
  • Revised Date : 2025-03-27
  • Accepted Date : 2025-03-28
  • DOI :https://doi.org/10.22765/pastj.20250001
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