Live Covid-19
United States 104,488,837
Cases: 104,488,837
Deaths: 1,136,313
Recovered: 101,575,749
Active: 1,776,775
India 44,683,454
Cases: 44,683,454
Deaths: 530,745
Recovered: 44,150,892
Active: 1,817
France 39,533,323
Cases: 39,533,323
Deaths: 164,286
Recovered: 39,288,720
Active: 80,317
Germany 37,822,577
Cases: 37,822,577
Deaths: 166,128
Recovered: 37,451,400
Active: 205,049
Brazil 36,868,946
Cases: 36,868,946
Deaths: 697,361
Recovered: 35,953,131
Active: 218,454
Japan 32,760,317
Cases: 32,760,317
Deaths: 69,601
Recovered: 21,608,715
Active: 11,082,001
South Korea 30,263,261
Cases: 30,263,261
Deaths: 33,614
Recovered: 29,894,362
Active: 335,285
Italy 25,488,166
Cases: 25,488,166
Deaths: 187,272
Recovered: 25,072,909
Active: 227,985
United Kingdom 24,293,752
Cases: 24,293,752
Deaths: 204,898
Recovered: 24,036,949
Active: 51,905
Russia 22,004,828
Cases: 22,004,828
Deaths: 395,319
Recovered: 21,384,884
Active: 224,625
Turkey 17,042,722
Cases: 17,042,722
Deaths: 101,492
Recovered:
Active: 16,941,230
Spain 13,740,531
Cases: 13,740,531
Deaths: 118,712
Recovered: 13,569,497
Active: 52,322
Vietnam 11,526,577
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Deaths: 43,186
Recovered: 10,614,591
Active: 868,800
Australia 11,312,904
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Deaths: 18,828
Recovered: 11,260,631
Active: 33,445
Argentina 10,037,135
Cases: 10,037,135
Deaths: 130,421
Recovered: 9,890,900
Active: 15,814
Taiwan 9,685,484
Cases: 9,685,484
Deaths: 16,750
Recovered: 9,228,369
Active: 440,365
Netherlands 8,583,669
Cases: 8,583,669
Deaths: 22,989
Recovered: 8,551,642
Active: 9,038
Iran 7,564,881
Cases: 7,564,881
Deaths: 144,764
Recovered: 7,337,869
Active: 82,248
Mexico 7,389,670
Cases: 7,389,670
Deaths: 332,475
Recovered: 6,626,983
Active: 430,212
Indonesia 6,731,304
Cases: 6,731,304
Deaths: 160,838
Recovered: 6,566,404
Active: 4,062
Poland 6,383,225
Cases: 6,383,225
Deaths: 118,748
Recovered: 5,335,940
Active: 928,537
Colombia 6,357,200
Cases: 6,357,200
Deaths: 142,544
Recovered: 6,181,431
Active: 33,225
Austria 5,803,370
Cases: 5,803,370
Deaths: 21,755
Recovered: 5,744,784
Active: 36,831
Greece 5,723,715
Cases: 5,723,715
Deaths: 35,822
Recovered: 5,672,479
Active: 15,414
Portugal 5,564,254
Cases: 5,564,254
Deaths: 26,033
Recovered: 5,534,306
Active: 3,915
Ukraine 5,373,104
Cases: 5,373,104
Deaths: 111,063
Recovered: 5,255,195
Active: 6,846
Chile 5,128,130
Cases: 5,128,130
Deaths: 63,895
Recovered: 5,059,720
Active: 4,515
Malaysia 5,037,995
Cases: 5,037,995
Deaths: 36,943
Recovered: 4,990,977
Active: 10,075
Israel 4,788,158
Cases: 4,788,158
Deaths: 12,203
Recovered: 4,770,957
Active: 4,998
North Korea 4,772,813
Cases: 4,772,813
Deaths: 74
Recovered: 4,772,739
Active: 0

Improving Tomato Yield in Greenhouse Agriculture Using Internet of Things (IOT) Technologies

Improving Tomato Yield in Greenhouse Agriculture Using Internet of Things (IOT) Technologies

ABSTRACT

The global food crisis bedeviling the polity as a result of dreadful climate change and insecurity has culminated also into poor tomato yield experienced by conventional farmers. Internet of things (IoT) has come in handy at this auspicious time to revolutionize agriculture and in turn improve tomato production. With the possibility of automated greenhouse smart farms, food scarcity will abate. This journal is aimed at developing an internet of things based precision monitoring system for improved greenhouse tomato in Nigeria. This work is predicated on the following objectives geared towards realizing the principal aim of this research. Reviews of recent related works to the subject under study were carried out to imbibe required knowledge for the work, this is swiftly followed by characterization and modeling of tomato yield in normal weather conditions with respect to temperature, humidity, soil moisture and yield. These variables were modeled in Simulink and the model was simulated to produce the characterized parametric values. Thereafter, the effects of either premium or deficiency of the variables and tomato yield were established. A rule base for precision monitoring and improving temperature, humidity and soil moisture in optimizing tomato yield was developed. A wireless sensor network was then modeled for precision monitoring and improving tomato yield in greenhouse agriculture. To actuate the activity of the system, an algorithm was proposed to implement the developed rule base and the sensing operation for precision and monitoring of improved tomato yield in greenhouse agriculture. All the models developed are then integrated and simulated and results generated from it. Finally, results are used to justify and validate the research. From the results, the temperature change from 760F to 83.250F gave a percentage improvement of 9.54%. Also, the humidity and soil moisture changed from 70% to 84% and 75% to 90% respectively. These give percentage improvement of 14% and 15% for humidity and soil moisture respectively. Similarly, tomato yield increased from 35 tons to 42 tons, giving a percentage improvement of 20%. From these results, the research can be said to have been justified and validated.

Keywords: Tomato Yield; Greenhouse Agriculture; Internet of Things (IOT)

Authorship
Ogoh, B. C. & Eke, J.

DOI Link: https://doi.org/10.5281/zenodo.7513008  | FULL PDF

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