Utilizing Machine Learning to Decode Growth Patterns and Yield Dynamics in Potato Cultivation

Abstract

Abstract Understanding growth patterns and yield dynamics in potato cultivation is essential for optimizing agricultural practices and improving productivity. This study leverages machine-learning techniques to analyze 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.

Code and data availability

The paper reports potato phenotyping (hyperspectral, growth, soil, and yield data) and ML analysis, but provides no authors' public URL or deposited identifier for its own datasets or code. The Data Availability Statement says data are available only on request; mentions of ICAR-CRIDA, USGS, CIP, etc. are generic third

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