Multilayer Perceptron Method of Artificial Neural Network for Classification of Farmers Based on Adoption of Drought Coping Mechanisms

Halagundegowda, G. R.

doi:10.18782/2320-7051.6405

Cited by 4 publications

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Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

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The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

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Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

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Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

Wambua

2022

Research Anthology on Artificial Neural Network Applications

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Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ³ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

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Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

Wambua

2019

International Journal of Applied Geospatial Research

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Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ≥ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

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Multilayer Perceptron Method of Artificial Neural Network for Classification of Farmers Based on Adoption of Drought Coping Mechanisms

Cited by 4 publications

References 0 publications

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

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