Technology has impacted the agriculture industry in many ways, from seedlings, cultivation, and crop harvesting to food processing, a crucial contributor to sustaining the rising world population. The United Nations FAO (Food and Agriculture Organization) projected that the world population would increase by another 2 billion from 7.5 billion to 9.7 billion in 2050, with more pressure on land as there will be only an extra 4% of land, which will come under cultivation by 2050. So, there is a need to improve food production efficiency by about 60% to feed the increased population. Consequently, an improved approach to agriculture is needed to increase production and reduce waste. Artificial Intelligence (AI) and Machine Learning (ML) are growing technological fields impacting various sectors and have practical applications in agricultural development. AI-driven solutions can enable farmers to improve efficiencies, quantity, and quality and ensure a faster go-to-market for crops.
AI/ML may be used in various contexts, including crop cultivation, harvesting, pest management and maintaining control of insects. The following are examples of how agriculture can benefit from AI or ML.
Precision Agriculture
In many parts of the world, agricultural practices are based on time-honoured customs, predicated on the notion that the wisdom and experience of older generations make their recommendations trustworthy. Farmers that use this strategy are subject to factors such as weather, which is becoming more unpredictable due to climate change and variations in rainfall patterns. Precision agriculture, enabled by AI and the Internet of Things, eliminates unpredictability in farming and assists modern farmers in optimising each phase of the agricultural process.
Species management
Many factors are at play when selecting suitable crop species, including nutrient regulation, climate change, disease resistance, etc. ML applications can be used to analyse multitudes of field data to aid the prediction of the right genes, considering climate conditions and also aids quick and accurate species classification by embedding morphology in its systems. AI applications include the protection of species through timely capture and transmission of livestock images via cloud-based technology. AI can also aid the prediction of seasonal movement of insects and birds migration, thereby keeping track of species, preventing loss, and increasing farming efficiency.
Yield Mapping
Using supervised ML techniques, yield mapping is used in agriculture to identify patterns in large data sets and understand the orthogonality of such patterns on a real-time basis. In terms of crop planning, this is an excellent resource of information. Knowing prospective yield rates well before the vegetation cycle begins is possible. Agricultural experts can now anticipate the potential soil yields for a particular crop by using a mix of machine learning algorithms to assess three-dimensional mapping, data on the social conditions collected from sensors, and data collected by drones on the colour of the soil.
Livestock management
AI facilitates using robots, drones, intelligent monitoring systems, and similar technologies to monitor farm animals’ health and detect injuries and disease.
ML’s added benefits include using innovative technology such as weight prediction systems to monitor the weight and optimise livestock diet. As well as providing facts and analytics through chatbots to answer queries about livestock’s wellbeing.
Optimised Pesticides
AI and ML are increasingly used in agriculture to find the best mix of biodegradable pesticides and limit their usage to the areas that need treatment, both of which help farmers save money and increase yields. Intelligent sensors and drone visual data streams enable agricultural AI programs to target selected areas. ML algorithms determine the ideal pesticide combination to control the risk of pests spreading and infecting healthy crops.
Alternative labour
Some agric companies are turning to AI and ML-based smart tractors, Agri-bots, and robots as feasible alternative labour. In the absence of sufficient human resources, large-scale agricultural operations must rely on robots to tend to tens of thousands of acres of crops while simultaneously ensuring the safety of their workers. Field yields may be increased by using self-propelled robot machines to spread fertiliser on each row of crops.
Managing Crops
The use of ML to detect disease and implement mitigations such as herbicides/pesticides over pre-defined areas and timescales. And the AI deployment of drones equipped with cameras/sensors to enable the efficient monitoring of crops and the use of the Internet of Things to deploy crop protection measures against pests.
Field conditions management
AI can ascertain the right conditions for crop growth and soil nutrient deficiencies and enable image recognition technology to improve harvest quality.
ML Algorithms can be used to analyse the soil by probing the moisture, temperature, and evaporation processes to understand the workings mechanisms of the entire ecosystem and find ways to restore the soil and produce healthy crops.
Final Words
We conclude this article with some quotes to highlight the potential future of smart agriculture.
“The agriculture industry has begun to use AI and ML to increase productivity, and it is expected to contribute another $2 to $3 trillion to the global GDP in the next few years while helping farmers to alleviate the current pressure due to the issue of food scarcity and also reducing the repercussion on the environment.”
“Global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is projected to triple in revenue by 2025, reaching $15.3 billion, according to BI Intelligence Research.”
“Spending on AI technologies and solutions alone in Agriculture is predicted to grow from $1 billion in 2020 to $4 billion in 2026, attaining a Compound Annual Growth Rate (CAGR) of 25.5%, according to Markets & Markets.”
“IoT-enabled Agricultural (IoT) monitoring is smart, connected agriculture’s fastest-growing technology segment projected to reach $4.5 billion by 2025, according to PwC.”
Food for thought!
Credits to Forbes and Dataquest for documenting the AI/ML applications presented above. There are many other applications which are not included in this article. The presented applications only provide a flavour for what’s possible or already in use.
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