Soybean Crop Growth and Yield Prediction using Deep Learning Model
DOI:
https://doi.org/10.26692/surjss.v57i02.7702Keywords:
Soybean, Crop Growth, Yield Prediction, Deep LearningAbstract
Agriculture is a fundamental pillar of food security and economic development, with soybean recognized globally for its high nutritional value and versatile applications in both food and non-food industries. Accurate soybean yield forecasting is critical for addressing key agricultural challenges, including efficient resource utilization, sustainable production, and climate change adaptation. Recent advancements in deep learning and remote sensing technologies, incorporating multi-source data such as UAV imagery, satellite data, and field measurements, have significantly enhanced yield prediction accuracy. This research proposes advanced deep learning models, utilizing spatial-temporal frameworks like CNN and LSTM, for precise, fast, and non-intrusive soybean yield assessment. Challenges such as dataset availability, computational demands, and regional variability in soil and climate are addressed to improve model flexibility and scalability. Furthermore, integrating hyperspectral, thermal, and RGB imagery enhances stress detection. This study contributes towards developing scalable, decision-support tools for sustainable and precision agriculture.
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