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How AI is Shaping the Future Harvest

The impact left by AI in such a short amount of time on numerous sectors has been great, but perhaps none have welcomed the technological shift as much as agriculture. The world’s oldest industry, the catalyst of modern civilization, is taking advantage of the plethora of digital interventions at its disposal. We can all learn from its industriousness.

Analyzing The Farm


At the core of agriculture is need and speed. Problems must be solved rapidly; disease, pests, temperature or soil variation all require modifications in the field, or a whole crop might be lost. The pressure is on, as demand for ruminant meat is set to increase by 90% by 2050 and all animal products by 70%. More broadly, there are projections which suggest a 50–60% increase in total global food demand by 2050, in order to feed growing populations. There is a labor shortage, there is less land and water to work with, and diseases are becoming more pervasive and complex, destroying up to 40% of crops worldwide every year. Traditional methods cannot keep up with the increased demand, so there is an existential imperative for farmers to turn to AI and automation in order to maximize their resources and reduce waste.

Agtech and Drone

Putting AI To Work


AI and more specifically data, has been exploited assiduously by farmers and AgTech leaders. Sensors, drones, satellites, robotics can all extract key information ranging from crop growth cycles to water content in soils. Feeding this deluge of data into AI models provides answers and predictions. The problem is the data is disparate and hard to gather. This has resulted in the development of platforms which collect and prosecute the intelligence from various sources on a farm or across multiple facilities, allowing farmers to plant and plough with unwavering accuracy. Narrowing down which crops need planting, when and where to irrigate, targeting a disease or pest or predator – all contribute to better ROI, while supporting sustainable farming and by extension leads to conservation of energy, water and material. 


Take the app, Tumaini, which enables photos of infected crops to be uploaded by growers, isolating where problems occur and encouraging the targeted spray of harmful chemicals or pesticides, as opposed to covering whole areas as a precaution. Images of compromised crops inform farmers where their problems are, saving them time and cost. Looking also at crop and farm management software, Taranis – drones feed visual crop information to AgTech providers, images are then annotated – classified and labeled in order to teach the AI model what features to recognize, which presents a more precise, complete picture to improve crop yields. The AI analysis can pinpoint insect damage, weed growth and growth of disease among other factors in crop health – the more comprehensive the data analysis, the greater the chance at an improved final product, and responsible use of water, soil and energy to go along with it.


Leveraging AI is more than just a trained model regurgitating information in a coherent style. It is a supply chain of knowledge. In agriculture, if we look at crop yield predictions, this may include monitoring plant health, nutrients in the soil, potential for disease, projections for crop output and forecasting prices in the market. At every stage sensors, satellite imagery or other various applications will feed information back to the AI model. Every input is verified or “trained” by humans – this way the AI algorithm can produce accurate findings. This is especially important in the case of unstructured data, a prominent source of information, and often uncollated, uncategorized and under-utilized.

Understanding the Crop of Data Available 


Unstructured data means information which is not predefined – it isn’t organized or stored in a specific format. This could include images, video, audio, qualitative survey results etc. In agricultural terms, this is often supplied by drones, satellites, crop surveys and relates to many practices on the farm, from yields to soils, crop health to water supply, while also containing business-pertinent information such as price, product, vendors, clients, payments etc. The challenge is that unstructured data is scattered and disorderly. To make sense of it, wheat must be separated from chaff – relevant data from irrelevant. 



There are platforms and applications which help source, clean, categorize and enrich the data available, which leaves only reliable takeaways. Deploying AI tools to rigorously unpack, cleanse and manage unstructured data saves massive amounts of energy and cost. In general, 90% data is unstructured and the time taken to sort through it can be prohibitive, but if properly plundered and packaged, it really can be the cream of the crop in terms of intelligence. 



Agriculture has become adept at rooting through unstructured data to produce actionable insights. AI algorithms draw on a constant feed of images, weather data, soil sensors, crop yield trends and much more, to create predictive analytics, helping determine what crops to plant, when to plant, how much to irrigate, and when to harvest. For an industry which is feeding the world, keeping us all alive, AI has been the difference-maker in sufficient and sustainable production.

Cultivating Ideas From the Field


Like other businesses, the ultimate goal for agriculture is to boost revenues from their products, and in a way which means they can keep growing, while sustaining their fields for generations to come. There can be many parallels drawn from the world of agriculture and transferred to a corporate setting. 

Monitoring and monetizing crops to harness maximum value is not too dissimilar from assessing how an e-commerce product is doing – who is viewing it, who is buying it, how it is reviewed, the qualitative information which can be retrieved from social media. Gaining a holistic understanding of the lifecycle of a product, as well as factors affecting its sale, can enable a business to target specific audiences, at select times, on the most appropriate platforms. 


The same logic can be applied to many other facilities. In manufacturing or construction for example – interrogating the assembly line, deriving insights from every moving part, material input, energy used, can support a more efficient, cost-effective and environmentally friendly operation.

Organizing Unstructured Data, Beyond the Farm 


What AI has made clear is that there is a whole ocean of data in a company’s back office, or a farmer’s field, which can help expedite the day-to-day functions, optimize resources, reduce tedious tasks and turn these efficiencies into profit and sustainable practices. The lion’s share of information held across an organization is unstructured, and needs to be assembled, sorted and analyzed. Compiling these rich sources of insight can be the difference between treading water and coursing through the sea on a speed boat. For agriculture, every facet of a farm is unearthed, digested and scrutinized to make improvements. If other industries adopted the same methods and approach to transform data into an asset, they would see a significant impact not just in their overall ROI, but in the performance of every role within the business.

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