COVER FEATURE
INGENIEUR
From IoT to I-IoT : Transforming Agriculture into Intelligent Agriculture
By Assoc . Prof . Dr Samsuzana Abd Aziz Dr Hazreen Haizi Harith Lai Zhi Yong Smart Farming Technology Research Center Universiti Putra Malaysia
The Internet of Things ( IoT ) has become
increasingly prevalent as enormous amount of sensing devices collect and / or generate various sensory data over time ; fundamentally changing the way we live , work and play .
IoT is a worldwide network based on standard communication protocols that connect physical devices to the Internet . By 2025 , it is estimated that there will be more than 21 billion IoT devices . This will lead to IoT market expansion in many IoT applications including agriculture . The agriculture IoT market is expected to grow at a compound annual growth rate ( CAGR ) of 14.1 % from 2019 to reach USD34.9 billion by 2027 . The installation of agricultural IoT devices is rising at a CAGR of 20 % worldwide , with the number of connected agricultural devices growing from 13 million in 2014 to 225 million in 2024 , as reports suggest .
Farmers and agricultural companies are turning to IoT for greater production capabilities to feed billions in the coming decades . The use of IoT in agricultural applications is not new . In advanced countries , IoT-based technology ranging from sensors on farm equipment , selfdriving tractors , drones and GPS imaging to weather tracking have long been adopted in the field . IoT in agriculture not only helps improve productivity and profitability but also paves the way for drastic changes in farm management and sustainable agriculture practices . According to Malaysian Investment Committee of Public Funds ( Jawatankuasa Pelaburan Dana Awam or JKPDA ), there are five main agricultural aspects that can be improved through IoT adoption :
● data collection ;
● internal process control for lowering production risk ;
● cost management and waste reduction ;
● process automation ; and
● enhanced product quality and volume .
For instance , real-time weather conditions like humidity , rainfall and temperature gathered from sensors deployed across the field can be synthesised to alert farmers if any disturbing weather conditions were found . Farmers can also monitor field conditions such as soil and nitrogen status from anywhere . If , for example , the soil moisture level decreases , farmers can decide to activate their irrigation system remotely . In greenhouse automation , smart irrigation scheduling , airflow and lighting can be controlled remotely by gauging parameters such as pressure , humidity , temperature and light levels in real-time through IoT sensors . In livestock management , farmers can monitor swine , cattle , broiler and milk production using cloud-based IoT tools and sensors deployed on the animals . For instance , sensor-equipped collar units and ear tags provide almost real-time insight into animal behaviour , herd location , walking time , grazing time , resting time and water consumption .
Nevertheless , the smart farming technologies discussed above utilise IoT that is limited to the connectivity between the “ Internet ” and “ things ”, following the traditional rule-based algorithm , with minimal or no intelligence . In recent years , predictive analytics involving big data or fast / real-time data streams has become a crucial
10 VOL 84 OCTOBER-DECEMBER 2020