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| Reference | Description | Challenges |
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| [7] | Soil moisture, air temperature, humidity, and light intensity of the farmland were observed to estimate the irrigation need. IoT is implemented for data monitoring and transmission of data takes place through short-range ZigBee technology. | (i) Soil temperature and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of ZigBee limits the design for shorter fields as being short-range technology and limited in scalability if very large farmland needs to be monitored. |
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| [8] | Arduino-based IoT enabled irrigation system. The developed system used temperature, pH, soil moisture, and humidity to estimate the irrigation need. | (i) Soil temperature and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of Arduino makes IoT design complex.
(iii) ML irrigation prediction is not part of system design.
(iv) System uses short-range wireless communication technology, which limits the utilization of system in larger fields. |
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| [23] | An IoT-based automated irrigation system is designed. It uses temperature, humidity, and soil moisture to estimate the irrigation requirement. | (i) Soil temperature and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of Arduino makes IoT design complex.
(iii) ML irrigation prediction is not part of system design. |
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| [24] | Based on soil moisture, an automated IoT enables irrigation system, which is proposed. The system enables the farmer to monitor the soil moisture and initiate the irrigation when required. | (i) Soil temperature, air temperature, and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of Arduino makes IoT design complex.
(iii) ML irrigation prediction is not part of system design. |
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| [25] | An IoT-based automated irrigation system is developed. Soil moisture, humidity, and temperature are the parameters observed to make decision regarding irrigation. Indirect method of estimating the crop’s water needs; that is, evapotranspiration has been used for irrigation scheduling. Developed system uses short-range RF communication technology for data transmission. System claims to be about 92% more efficient in comparison with tradition irrigation techniques. | (i) Soil temperature and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of Arduino makes IoT design complex.
(iii)ML irrigation prediction is not part of system design.
(iv) System uses short-range wireless communication technology, which limits the utilization of system in larger fields. |
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| [26] | IoT-based irrigation system is developed by observing soil moisture and uses GSM technology for monitoring and Arduino as development platform | (i) Soil temperature, air temperature, and wind conditions are important factors in irrigation planning but neglected in design.
(ii) Use of Arduino makes IoT design complex.
(iii) ML irrigation prediction is not part of system design.
(iv) Use of GSM makes system complex and costly as require placing mobile phone at site with active SIM. |
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| [27] | An intelligent Raspberry Pi-based irrigation system is developed. Soil moisture and air temperature are the observed farm parameters. KNN algorithm is applied to observed farm parameters in order to make prediction for irrigation requirement by the crops. | (i) Soil temperature and wind conditions are important factors in irrigation planning but neglected in design.
(ii) The system developed does not discussed regarding sensor network for monitoring larger fields.
(iii) The design does not make any effort to identify the best ML algorithm for the irrigation application based on observed parameters. |
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