IoT and AI Based Smart Soil Quality Assessment for Data-Driven Irrigation and Fertilization

Abstract

Purpose: The rapidly growing demand for food due to rapid population growth in East Africa is one of the challenging issues and the sustainable way of tackling it, is to enhance the agriculture activities to satisfy the need of increasing farm productivity. However, the climate change, limited water resources and poor soil fertility reduces crops yields. In attempt to solve these challenges, Internet of thing (IoT) in conjunction with artificial intelligence (AI) techniques is increasingly being used in agriculture sector. This study investigates an integration of IoT and a deep learning (DL) driven solution for smart irrigation and fertigation by assessing soil nutrients and soil water content dynamics in Eastern province of Rwanda for optimization of these scare resources while increasing yields productivity.
 Methodology: The research data for analysis was collected from KABOKU-KAGITUMBA irrigation scheme, and data on soil moisture and soil nutrients was gathered over a six-month period from 36 sensor nodes that were installed in approximately 70 hectares with 6 pivots for irrigation. The collected data in real time by sensors was sent to an IoT platform and incorporated with the forecasted weather information there after a deep learning based model used to predict when to irrigate and when to fertigate and the notification sent to farmer with recommendations. The irrigation valves were automatically actuated based on the predictions. The study's main software tools for gathering, displaying, and analyzing real-time data streams were Things Speak, Tensor Flow Lite, and the Arduino Software (IDE). A prototype was finally implemented effectively.
 Findings: The resulting model showed that can perform well with an accuracy of 91.7% and it can work well when deployed in the remote area with minimum internet connection.
 Unique Contribution to Practice: since the currently technologies used in irrigation and fertilization are manual or based on threshold values for automatic irrigation, we recommend the implementation of this solution since it will guarantee data-driven farming, which will help to protect the environment and ensure the optimization use of water resources. Additionally, this will result in lower operating cost, which will raise earnings.

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“…This synergy enhances efficiency and sustainability by combining the locational accuracy of GPS with the analytical prowess of GIS. Such integration allows for precise seedling placement and optimized irrigation practices, as evidenced by the deployment of soil sensors and nutrient management strategies ( Nyakuri et al., 2022 ). Moreover, the advent of automation in vegetable farming, highlighted by the introduction of advanced feeding devices and transplanters, aligns with this technological evolution, further contributing to increased efficiency ( Dadios et al., 2022 ).…”

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

“…Smart phones was used to communicate in this solution and the amount of water they require at different phases of growth are taken into account in the solution so that the field is irrigated in accordance with the calculated water need by the plant [23] [24]. The integration of IoT devices, sensors, and data analytics enables real-time monitoring, data-driven decision-making, and automated control in irrigation and fertigation systems as developed in [4][21] [25]. Soil condition-based Irrigating alone to meet the water requirements of the plants may not result in the appropriate levels of moisture required, the weather patter variation also have to be considered.…”

“…As a result, there is an urgent need to develop innovative solutions that can improve agricultural efficiency and sustainability. One such solution is the integration of Artificial Intelligence (AI) in conjunction with Internet of Things (IoT) technologies into irrigation and fertigation systems, coupled with advanced weather prediction capabilities that holds great potential to enhance agricultural efficiency and sustainability [4].…”

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