English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Accurate estimation of Reference Evapotranspiration (ET0) is important for efficient management and conservation of irrigation water. Existing methods of ET0 rate determination are complex for application at the farmer level. Apart from standard methods of ET0 determination, many data-driven soft computing approaches were also proposed to determine the ET0 with limited data set. We proposed a temperature and humidity-based ML approach for ET0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET0) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET0-rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92&#x0025; accuracy. The KNN model of ET0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16&#x0025; and Mean Absolute Errors (MAE) by 3&#x0025; against the state of the art approach.

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT