Smart irrigation system for the reinforcement of Precision agriculture using prediction algorithm: SVR based smart irrigation

Sirisha, V.V.S.S; Sahitya, G.

doi:10.1109/icict50816.2021.9358524

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Garg Satvik et al [22] propose IoTbased soil moisture and nutrients monitoring for irrigation water and fertilizer recommendation with a pre-trained Convolution Neural Network (CNN). Sirisha and Sahitya [23] propose ET prediction with IoT-assisted soil moisture monitoring with help of Kernel Canonical Correlation Analysis (KCCA) by using the Support Vector Machine (SVM) with kernel function for smart irrigation water scheduling. Pincheira et al [24] propose energy-efficient IoT and blockchain-based smart irrigation water scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

et al. 2022

View full text Add to dashboard Cite

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…GRID SEARCH RESULTSModelBest score Best parametersLinear regression[25] 0.586132 {'copy_X': True, 'fit_intercept': True}Decision tree[26] 0.909513 {'criterion': 'squared_error', 'splitter': 'best'} Support vector regressor[27] 0.720655 {'C':10, 'epsilon':0.2, 'kernel': 'linear'}…”

mentioning

confidence: 99%

Precision Agriculture based on Machine Learning and Remote Sensing Techniques

Alaieri

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices.

show abstract