Agriculture is fundamental to human civilization, yet it faces immense challenges in meeting escalating global food demands while balancing optimal resource utilization and environmental protections. Fortunately, emerging technologies like artificial intelligence (AI), Internet of Things (IoT) sensors, and edge computing promise transformational capabilities advancing precision agriculture to new heights through data-driven insights.
When integrated properly, AI and IoT enable cleaner food production across vectors from crop yield forecasting, automated irrigation timing, predictive pest and disease modeling, and operational analytics on farm vehicle fuel efficiency. Together these exponentially improving technologies anchor agriculture’s digital transformation through enhanced production capacity, resource efficiency, and supply chain transparency. They accelerate solutions to urgent climate-driven food security challenges as global populations march towards 10 billion by 2050. AI and IoT can sustainably unleash agriculture’s full potential.
AI for Predictive Analytics
Previously farmers lacked robust predictive capabilities anticipating how upcoming weather patterns, pest development cycles, or yield potentials respond to applied fertilizer and cultivated acre types. But now aggregated data lakes unite cross-regional crop development models with weather forecasts and soil nutrition readings creating powerful predictive engines estimating production risks and outcomes. This foresight accurately simulates scenario decisions like investing in crop protection ahead of severe rainfall or pruning crops projecting diminished yields from early growth readings.
Farmers can even simulate yield impact and profit differentials across candidate seed products, regional harvest schedules, and equipment investments tailored to unique soil composition and microclimate variabilities individual to each operation. AI simulation models enable informed planning optimized balancing resource costs with yield and calendar considerations way beyond reactive best guesses. Prescriptive recommendations further guide application rates by dispensing precise fertilizer, pesticide, and harvester loads specific to localized needs rather than generalized approximations. This data-driven precision promises optimizing finite resources amidst booming food demands while minimizing environmental impacts through smart reductions eliminating excessive waste permeating 20th century farming conventions.
AI and IoT in Agriculture for Automating and Monitoring
In addition to analytics guiding human decisions, AI and IoT in Agriculture automation actuate physical responses like triggering irrigation systems, routing harvesters, and adjusting growth conditions without any manual oversight requirement. Hundreds of Internet-connected sensors embedded across vast farmlands continually relay soil moisture data, storage tank levels, crop ripeness, and equipment statuses to cloud platforms running decision algorithms that issue commands like opening remote access valves when moisture drops below-defined thresholds. Physical automation creates continuously optimized environments for crops and livestock benefiting from system networked intelligence.
Predictive Maintenance for Harvest Productivity
Continuous equipment monitoring further enhances harvest productivity by predicting failures based on usage metrics and preventatively scheduling maintenance like automotive telematics do rather than allowing outright breakdowns during critical seasonal windows.
Converging Physical and Digital Agriculture
Sensor data transmitted from device CPU logs combined with computer vision scans assessing wear onsite direct ideal predictive part replacement and proactive repairs keeping machinery turnover maximized. This sustains farm output and revenues avoiding costly disruptions through digitally enabled reliability. Together IoT oversight and automation fuse the physical and digital agriculture world into a converged infrastructure benefiting both farmers and consumers universally.
How AI and IoT Are Impacting Agriculture?
Many of the world’s population is still undernourished. Food insecurity is still a major problem plaguing the world, particularly the Third World. That’s because of unpredictable rainfall, nutrient runoff, new strains of pests and diseases, and climate change.
To address these challenges to food production, scientists are turning to smart farming. Thanks to incredible advances in artificial intelligence (AI) and Internet of Things (IoT) devices, smart farming is no longer a distant dream. We at Saiwa explore the phenomenon of smart farming in this paper. Smart farming is the use of AI based technologies, including UAVs, machine learning and robotics to improve farming operations, IoT brings all these technologies together.
What are the main benefits of using AI and IoT in Agriculture?
The use of AI and IoT in agriculture ensures healthier crops by monitoring soil, temperature, humidity and crop-specific growing conditions. Real-time updates from continuous monitoring ensure less crop waste. Farmers can automate a wide range of farming tasks using the data they collect. The data collected can also be used to study trends. AI systems can use historical crop yield data and compare it with current data to accurately predict crop yields over time.
Data-driven forecasting can help improve agribusiness returns and the functioning of the food supply chain. Smart IoT applications can also help control costs associated with excessive use of resources such as water and electricity. They can ensure timely irrigation and pest control from anywhere through connected devices and automated systems. IoT creates a data-driven system that farmers can rely on to manage their farms. These reduce human labor and increase the quality and quantity of agricultural produce.
Challenges of using AI and IoT in Agriculture
Despite the benefits, there are a number of challenges associated with smart farming. The most important is that smart farming has huge upfront and operational costs to build up the required infrastructure. The farmers could meet the upfront costs through the availability of loans or may offset the operating costs through increased productivity than conventional farming methods.
According to a recent WHO report, around three million cases of pesticide poisoning occur each year, resulting in nearly 220,000 deaths in developing countries, making the case for pesticide-free organic farming all the more compelling. This is a case of organic farming in a closed indoor environment, where the climate can be closely maintained without the threat of pests found in open-field farms.
An immediate threat to smart agriculture will come from computer hackers attacking smart, self-driving agricultural machinery such as smart tractors and UAVs. IoT sensor node communication using various IEEE wireless communication standards is particularly vulnerable to Denial of Service (DoS) attacks. Similarly, DoS attacks on servers hosting historical data logs for smart agriculture and other agricultural advisory systems will keep smart farmers in the dark about timely action against natural disasters (pest infestations). Robust counter-attack techniques against these cyber threats need to be present at both the core (servers) and the edge (smart farming machines and IoT field nodes) to protect the interests of smart farmers.
Read more: Practical Application & Future of AI in Agriculture
Conclusion
The symbiotic integration of AI and IoT in agriculture heralds a transformative era marked by heightened efficiency and sustainability. The deployment of AI for predictive analytics empowers farmers with foresight, enabling informed decision-making based on data-driven insights. The amalgamation of AI and IoT in agriculture for automation and monitoring ushers in a new paradigm where physical processes seamlessly synchronize with digital intelligence, optimizing resource utilization and enhancing overall productivity. As agriculture undergoes this technological metamorphosis, the profound impacts are evident, ranging from predictive maintenance to real-time crop monitoring. The accrued benefits encompass heightened yields, resource optimization, and sustainable practices. However, this paradigm shift is not without its challenges, including data security and integration complexities. Navigating these challenges is imperative for realizing the full potential of AI and IoT in revolutionizing global agriculture.
