Utilising Machine Learning and Hyperspectral Data to Decode Growth Patterns, Cultivar Identification and Yield Dynamics in Potato Cultivation

Abstract

Understanding growth patterns and yield dynamics in potato cultivation is essential for optimising agricultural practices and improving productivity. This study leverages machine learning techniques to analyse and predict potato growth stages and yield outcomes based on environmental and physiological data. Various machine learning models were developed using multispectral imaging, soil parameters and climatic factors collected across diverse cultivation environments. The models were evaluated for their accuracy in identifying growth stages and forecasting yield performance. Key physiological trends were identified during the tuber initiation, bulking and maturation phases, correlating with specific environmental conditions. Predictions for tuber yield showed high accuracy, with models achieving R² values above 0.90 across validation datasets. Additionally, the study highlights the importance of integrating machine learning with precision agriculture systems to enhance decision-making and resource management. The proposed methodology demonstrates significant potential for advancing potato-farming practices by providing actionable insights into growth and yield optimization.

Plant phenotyping relevance

マルチスペクトル画像と機械学習を用いてジャガイモの生育段階と塊茎収量を推定し、検証しており、植物形質の取得・推定手法が主要な貢献です。

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