A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making

Sujata Patil; Kalyanapu Srinivas; Kothuri Parashu Ramulu

doi:10.2478/jsiot-2024-0004

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A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making

Journal of Smart Internet of Things

Volume 2024 (2024): Issue 1 (June 2024)

By: Sujata Patil, Kalyanapu Srinivas and Kothuri Parashu Ramulu

Open Access

|Jun 2024

Figures & Tables

Convergence Assesment for the Various Optimization Strategies

Table 1:

Performance Metrices

SL.NO	Performance Measures	Expression
1	Accuracy	$\frac{T P + T N}{T P + T N + F P + F N}$
2	Recall	$\frac{TP}{TP + FN} \times 100$
3	Specificity	$\frac{T N}{T N + F P}$
4	Precision	$\frac{T N}{T P + F P}$
5	F1-Score	$2. \frac{Precison * Recall}{Precison + Recall}$

Table 2:

Evaluation of Various Models for Predicting Crop Yield Productivity with Dropout Rate of 0.2

Algorithm	Performance Metrics(%)		recall	Specificity	F1-score
Algorithm	Accuracy	Precision	recall	Specificity	F1-score
LSTM	82.5	83.5	83.4	84.1	84.2
LSTM+PSO	86.3	86.2	85.8	86.1	85.4
LSTM+GA	86.6	86.4	86.2	87.3	85.9
LSTM+WOA	88.4	87.3	86.8	87.1	86.9
LSTM+SSO	88.5	89.1	88.8	88.4	88.5
LSTM+SHO	91.6	89.7	89.4	89.4	88.4

Table 3:

Benchmarking assessment of the various methodologies in identifying the crop-yield productivity with the drop-out=0.4

Algorithm	Performance Metrics(%)		recall	Specificity	F1-score
Algorithm	Accuracy	Precision	recall	Specificity	F1-score
LSTM	84.3	83.6	83.5	83.5	84
LSTM+PSO	87.6	86.3	85.9	88.3	85.4
LSTM+GA	87.5	86.5	86.3	88.3	85.9
LSTM+WOA	89.5	87.4	86.9	89.6	86.9
LSTM+SSO	89.4	89.0	88.9	89.9	88.5
LSTM+SHA	91.0	89.8	89.5	90.8	88.4
PROPOSED MODEL	97.3	96.9	96.7	96.9	96.5

Table 4

Benchmarking assessment of the various methodologies in identifying the crop-yield productivity with the drop-out=0.6

Algorithm	Performance Metrics(%)		recall	Specificity	F1-score
Algorithm	Accuracy	Precision	recall	Specificity	F1-score
LSTM	83.5	83.6	83.5	84.0	84
LSTM+PSO	88.3	86.3	85.9	86.0	85.4
LSTM+GA	88.3	86.5	86.3	87.4	85.9
LSTM+WOA	89.6	87.4	86.9	87.2	86.9
LSTM+SSO	89.9	89.0	88.9	88.5	88.5
LSTM+SHO	90.8	89.8	89.5	89.5	88.4
PROPOSED MODEL	97.6	96.9	96.7	96.6	96.5

Table 5

Training Accuracy Performance using the no of batches =160

Sl.no	No of batches	No of Epochs	Training Accuracy (%)
01	160	40	96.4%
02	160	80	97.6%
03	160	120	98.6%
04	160	160	98.51%
05	160	200	98.4%
06	160	240	98.32%
07	160	280	98.30%

Table 6

Training Accuracy Performance using the no of batches =160

Sl.no	No of batches	No of Epochs	Testing Accuracy (%)
01	160	40	96.36%
02	160	80	97.65%
03	160	120	98.55%
04	160	160	98.35%
05	160	200	98.21%
06	160	240	98.3%
07	160	280	98.2%

Table 7

Validation/Verification Accuracy Performance using the no of batches =160

Sl.no	No of batches	No of Epochs	Validation /Verification Accuracy (%)
01	160	40	96.34%
02	160	80	97.6%
03	160	120	98.55%
04	160	160	98.35%
05	160	200	98.21%
06	160	240	98.3%
07	160	280	98.2%

Table 8

Performance Metrics Evaluation for the Proposed Architecture using Testing Datasets

Sl.no	No of batches	No of Epochs	Precision (%)	Recall(%)	F1-Score
01	160	40	96.4%	96.1%	96.3%
02	160	80	96.43%	96.31%	96.3%
03	160	120	97.6%	97.41%	97.52%
04	160	160	97.53%	97.12%	97.32%
05	160	200	96.4%	96.3%	96.2%
06	160	240	96.3%	96.2%	96.1%
07	160	280	96.2%	96.1%	96.1%

Table 9

Performance Metrics Evaluation for the Proposed Architecture using Verification/Validation Datasets

Sl.no	No of batches	No of Epochs	Precision (%)	Recall(%)	F1-Score
01	170	40	96.5%	96.%	96.3%
02	170	80	96.45%	96.3%	96.3%
03	170	120	97.5%	96.9%	97.51%
04	170	160	97.3%	97.1%	97.31%
05	170	200	96.45%	96.30%	96.4%
06	170	240	96.30%	96.20%	96.20%
07	170	280	96.20%	96.10%	96.17%

References

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Articles in this issue

DOI: https://doi.org/10.2478/jsiot-2024-0004 | Journal eISSN: 2956-8323

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Language: English

Page range: 46 - 59

Submitted on: Feb 22, 2024

Accepted on: May 1, 2024

Published on: Jun 15, 2024

Published by: Future Sciences For Digital Publishing

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

Harris Hawk Optimization,

Residual Long Short-Term memory,

Climatic Changes

Related subjects:

Engineering,

Introductions and overviews,

History of engineering,

Electrical engineering,

Fundamentals of electrical engineering,

Electronics,

Information technology

© 2024 Sujata Patil, Kalyanapu Srinivas, Kothuri Parashu Ramulu, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 2024 (2024): Issue 1 (June 2024)

A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making

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