Herbicides face stricter
regulation and in some cases are being banned
AGRICULTURE 4.0
G iven the challenges ahead there is no doubt: Business as usual will not work. Agriculture and food systems must change dramatically. The bad news is: This will be expensive, with FAO saying that US$265 billion is needed globally per year to end hunger. The good news is: The transformation has already begun. Over the past 50 years, the green revolution has enabled the production of cereal crops to triple with only a relatively small increase in the area of land under cultivation. The combine harvester ushered in an era of intensive, industrialized farming – and the world has come a long way since its invention in the 1830s. Today autonomous tractors, robots tending to crops and drones precisely dispersing inputs are a big leap forward from 20th century farms. Thanks to the Fourth Industrial Revolution that has supplied every industry with new technologies, agriculture too is undergoing revolutionary changes. Experts have dubbed it Agriculture 4.0 and three general trends have been identified, where technology is disrupting the industry: l Produce differently using new techniques. l Use new technologies to bring food production to consumers, increasing efficiencies in the food chain. l Incorporate cross-industry technologies and applications. While innovations create excitement about tech’s potential on the farm, they only scratch the surface of how technology can help to tackle pressing challenges like climate change and food supply constraints. With AgriTech investments at an all-time high, start-ups and major players are thinking about ways to apply innovations like Artificial Intelligence (AI) across the entire agricultural value chain. These emerging applications could shape the future of agriculture. AI can improve the earliest phase of the agricultural lifecycle: creating better crop inputs before seeds are in the ground. For example, the gene- editing technology CRISPR could help to design more resilient, high-yield seeds. Companies are applying AI to improve
CRISPR's speed and efficacy. Because many crops are so genetically complex – corn has 32,000 genes compared to 20,000 in humans – AI is invaluable in helping researchers understand the effects of editing multiple genes. Companies are already using these technologies to bump up crop yields while requiring less water and other inputs. Increasing yields of staple crops like corn, soy and wheat is critical. Meanwhile, combining DNA-encoded libraries and machine learning models is helping to identify new solutions to protect crops from pests. Tailored, resilient seeds and crop protection for the evolving growing needs of each region can create a more stable web of staple crops, lessening the dependency on global food supply chains and strengthening local resilience. Weed control is essential for improving crop yields, but it is getting increasingly difficult. Some weeds are becoming resistant to herbicides, which face stricter regulation and in some cases are being banned. When chemicals are required on crops, both tractor-towed systems and agribots could apply microdoses to the individual plants that require them, rather than spraying an entire field. Some trials have suggested microdosing could reduce the amount of herbicide being sprayed on a crop by 90 percent or more. Weeding is a chore that most farmers would happily hand to robots. But for a robot to do the job properly it must be able to distinguish a weed from what is being cultivated. That is becoming easier with advances in computer vision. Agribots, driverless tractors and other types of farm automation form an industry that is expected to grow at around 23 percent a year and to be worth more than US$20 billion by 2025, according to the research firm MarketsandMarkets. Self-contained agribots will have to compete with systems towed by smart tractors. Most modern tractors and combine harvesters can steer themselves across fields using satellite positioning and other sensors. Some tractors use digital maps of crops obtained by satellites and drones to highlight the places that require fertilizer or pesticides. Big tractor producers are
developing fully autonomous tractors. As climate change effects worsen, growers need detailed real-time data to determine exactly how and when to treat their crops. AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones. The amount of data captured by smart sensors and drones providing real-time video streaming gives agricultural experts entirely new data sets they have never had access to before. It is now possible to combine in-ground sensor data of moisture, fertilizer and natural nutrient levels to analyze growth patterns for each crop over time. Machine learning can harvest massive data sets to give advice on how to optimize crop yields. Sensors are another valuable tool. They gather data pinpointing threats to a crop, like dehydration or disease, in a specific area – allowing a farmer to apply crop protection, water or nutrients only in that area. Depending on farmers’ circumstances and needs, they can select other technologies to pair sensors with. Connecting sensors with virtual reality can create crops’ “digital twins.” Growers can use these to access their fields from anywhere and make informed decisions based on real-time data. Using sensor data to inform precision spraying of safe, effective crop protection can produce higher yields of healthier crops. In precision agriculture, real-time weather forecasting helps farmers with day-to-day decisions on when and how much to irrigate, fertilize and apply pesticides to their crops. Controlled-environment agriculture promises to further reduce the impact. Some smart greenhouses are completely automated, run by algorithms that ensure optimal conditions for plant growth by adjusting inputs like roof ventilation, artificial lighting and heating. Ultra-high resolution imaging can spot early symptoms of disease, water stress and soil degradation, while drones spray fertilizer, pesticides and water with pinpoint accuracy. By reducing the
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