Progressive moisture prediction technique using regressive learning using soil and vegetation data Online publication date: Mon, 08-Jan-2024
by Xuetao Jia; Ying Huang
International Journal of Sensor Networks (IJSNET), Vol. 43, No. 4, 2023
Abstract: Predicting soil moisture is crucial for optimal crop planting and improved yields across varying climates and soil types. Recent intelligent computing trends have enabled machine learning methods to predict soil moisture. This article introduces a progressive moisture prediction technique (PMPT) using regressive learning (RL) to predict soil moisture for precision farming. PMPT addresses prediction error from missing soil and vegetation sensor data. RL identifies and estimates missing data based on previous predictions. Prediction error margins are identified between vegetation yields using linear progression. PMPT is validated based on prediction values close to the error margin and accurate crop yield outputs. Factors like climate and maximum moisture periods are incorporated to compute minimum and maximum values around crop cycles. Thus, RL differentiates accurate and erroneous moisture detection from linear soil inputs regardless of data availability. PMPT is validated on metrics including prediction accuracy, error, time, differentiation, and saturation point.
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