<p>Understanding the crop growth and yield response to variable irrigation and the relationship between crop eco-physiological and morphological parameters is critical for identifying a balanced irrigation management strategy and developing decision support systems for early detection and information for on-ground decisions. The Internet of Things model was constructed to be used in the permanent and continuous monitoring of plants through various measurements. The present study aimed to optimize water use, monitor environmental factors, and improve crop yield. The field experiment was conducted in a greenhouse at SEKEM Company for Biodynamic Agriculture in Egypt during the winter season of 2022–2023. Green bean plants (<i>Phaseolus vulgaris</i> L.) were cultivated under two irrigation systems: surface drip irrigation (SDI) and subsurface drip irrigation (SSDI), with three irrigation levels of 100%, 80%, and 60% of the crop water requirements (CWR). This resulted in a total of six experimental treatments (T): T1, T3, and T5 for SDI; and T2, T4, and T6 for SSDI. An Internet of Things (IoT) unit equipped with sensors was installed to monitor air temperature, air humidity, and soil moisture, enabling the calculation of vapor pressure deficit (VPD). Data were collected daily throughout the growth stages. IoT unit with sensors were installed to monitor air temperature, air humidity, and soil moisture, enabling vapor pressure deficit (VPD) calculations. The measurements were collected daily during the growth stages. The field trial results demonstrated that SSDI at 100% CWR (T2) was recorded the lowest VPD values (1.34, 0.67, and 2.10 kPa) during development, Mid, and Late stage, respectively, while SDI at 60% CWR (T5) recorded the highest (1.77, 1.19, and 2.72 kPa) during development, Mid, and Late stage, respectively. Canopy temperatures were lower under SSDI (T2) compared to SDI (T5). Green bean yield significantly varied between irrigation methods. SDI yield ranged from 25.425 to 28.719 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{t ha}}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>t ha</mtext> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>, with the highest (28.719 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{t ha}}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>t ha</mtext> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>) at 80% CWR (T3). while the SSDI yield ranged from 24.390 to 33.614 <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{t ha}}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>t ha</mtext> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>, with the maximum 33.614 <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{t ha}}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>t ha</mtext> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </math></EquationSource> </InlineEquation> recorded under 80% CWR (T4). Water productivity (WP) varied significantly, from 7.66 to 15.73 <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq5.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{kg m}}^{-3}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>kg m</mtext> </mrow> <mrow> <mo>-</mo> <mn>3</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>. The highest WP (15.73 <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq5.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{kg m}}^{-3}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>kg m</mtext> </mrow> <mrow> <mo>-</mo> <mn>3</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>) was recorded under SSDI at 60% CWR (T6), while the lowest (7.66 <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44447_2025_39_Article_IEq5.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">${\text{kg m}}^{-3}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>kg m</mtext> </mrow> <mrow> <mo>-</mo> <mn>3</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>) occurred under SSDI at 100% CWR (T2). The study underscores IoT-based precision irrigation’s potential to maximize yields and enhance water efficiency, with SSDI proving more efficient at lower irrigation levels.</p>

Optimizing water management in greenhouse farming through an IoT-enabled monitoring system

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2025年8月14日

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Abdelrahman A. El-Sheshny,
Amal M. Abdel-Hameed,
M. A. Al-Rajhi,
Hamed G. Ghanem,
Taha M. Elzanaty,
Mostafa H. Fayed

摘要

Understanding the crop growth and yield response to variable irrigation and the relationship between crop eco-physiological and morphological parameters is critical for identifying a balanced irrigation management strategy and developing decision support systems for early detection and information for on-ground decisions. The Internet of Things model was constructed to be used in the permanent and continuous monitoring of plants through various measurements. The present study aimed to optimize water use, monitor environmental factors, and improve crop yield. The field experiment was conducted in a greenhouse at SEKEM Company for Biodynamic Agriculture in Egypt during the winter season of 2022–2023. Green bean plants (Phaseolus vulgaris L.) were cultivated under two irrigation systems: surface drip irrigation (SDI) and subsurface drip irrigation (SSDI), with three irrigation levels of 100%, 80%, and 60% of the crop water requirements (CWR). This resulted in a total of six experimental treatments (T): T1, T3, and T5 for SDI; and T2, T4, and T6 for SSDI. An Internet of Things (IoT) unit equipped with sensors was installed to monitor air temperature, air humidity, and soil moisture, enabling the calculation of vapor pressure deficit (VPD). Data were collected daily throughout the growth stages. IoT unit with sensors were installed to monitor air temperature, air humidity, and soil moisture, enabling vapor pressure deficit (VPD) calculations. The measurements were collected daily during the growth stages. The field trial results demonstrated that SSDI at 100% CWR (T2) was recorded the lowest VPD values (1.34, 0.67, and 2.10 kPa) during development, Mid, and Late stage, respectively, while SDI at 60% CWR (T5) recorded the highest (1.77, 1.19, and 2.72 kPa) during development, Mid, and Late stage, respectively. Canopy temperatures were lower under SSDI (T2) compared to SDI (T5). Green bean yield significantly varied between irrigation methods. SDI yield ranged from 25.425 to 28.719 ${\text{t ha}}^{-1}$ ${t ha}^{- 1}$ , with the highest (28.719 ${\text{t ha}}^{-1}$ ${t ha}^{- 1}$ ) at 80% CWR (T3). while the SSDI yield ranged from 24.390 to 33.614 ${\text{t ha}}^{-1}$ ${t ha}^{- 1}$ , with the maximum 33.614 ${\text{t ha}}^{-1}$ ${t ha}^{- 1}$ recorded under 80% CWR (T4). Water productivity (WP) varied significantly, from 7.66 to 15.73 ${\text{kg m}}^{-3}$ ${kg m}^{- 3}$ . The highest WP (15.73 ${\text{kg m}}^{-3}$ ${kg m}^{- 3}$ ) was recorded under SSDI at 60% CWR (T6), while the lowest (7.66 ${\text{kg m}}^{-3}$ ${kg m}^{- 3}$ ) occurred under SSDI at 100% CWR (T2). The study underscores IoT-based precision irrigation’s potential to maximize yields and enhance water efficiency, with SSDI proving more efficient at lower irrigation levels.