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Silicon Snack: Precision Farming Fueled by Big Data

July 15, 2019

Food security is a growing concern. With the world’s population forecast to increase from 7.6 billion in 2017 to 9.8 billion by 2050, it’s estimated that an additional billion metric tons of cereal and 200 million metric tons of meat will be needed annually to satisfy demand. Meanwhile, available arable land is predicted to expand by 5 percent over the same period, while water scarcity is also a concern, in part due to the expanding population. Farmers face unprecedented pressure to reduce waste and costs and increase yields. Sensors and data are enabling new precision farming techniques to help address this complex challenge.

Sensors improve irrigation 

Solutions using sensors and cloud platforms can enable precision irrigation and fertigation to save water, decrease production costs, and improve yields. Unsurprisingly, many originate from areas where water is scarce.

  • The autonomous irrigation system from Israel’s Tevatronic combines sensors next to plants, a cloud server and valve switch controller to analyze data about air temperature and relative humidity and make decisions about opening and closing irrigation valves. This avoids overwatering or underwatering as it effectively relies on the plants to dictate their irrigation schedule.
  • NetBeat™Agri-Cloud from Israeli company, Netafim, takes real-time data from field sensors plus weather and satellite imaging data sources and analyzes it in the cloud against a set of proprietary Dynamic Crop Models to generate alerts and recommendations. Farmers can interface with the system via smartphone.
  • Australia’s Observant, part of Jain Irrigation, helps farmers use data analytics to understand crop growth, analyze water demand, and remotely schedule irrigation.
  • Global tech giant IBM has worked with E&J Gallo Winery in California using Watson® to analyze temperature, soil pH and other environmental data from IOT sensors and satellites to deliver highly precise irrigation per grapevine, reducing water consumption by 25 percent.

Accurate spraying of herbicide

See & Spray machines from California’s Blue River Technology use computer vision and machine learning based on millions of photos to tell the difference between a plant and a weed. As the machine moves through the crop, it decides on a plant-by-plant basis whether to apply herbicide, reducing chemical costs for a mid-sized farmer by a factor of ten. The machines are already in use for weeding cotton and soybean crops.

Fighting plant disease

In India, Tata Consultancy Services (TCS) is using humidity, temperature and rainfall data gathered from a wireless sensor network across farms to help farmers determine disease risk for potato blight.

Other innovations include machine learning to crunch sensor data about a plant’s vitals to predict which pests might attack;  smart greenhouses with intelligent climate control; and wireless IoT applications to monitor location and health of cattle. Wireless alert sensor, Moocall, even detects when a cow is going into labor and alerts the farmer.

Importantly, data solutions can be applied by small-scale farmers in some of the most challenging environments for agriculture, such as in Africa. Startups like Zenyus in Nigeria and UjuziKilimo in Kenya, are using data analytics to analyze soil data and advise farmers on fertilizers and seeds and how to optimize irrigation, while building knowledge-sharing communities with mobile technology. As the agriculture industry tackles the issue of food security in the coming years, sensors and data will be at the heart of solving the problem.

Editorial Note: The use of any company, product, or trade names is for descriptive purposes only and does not imply endorsement by Lam Research Corp. or its subsidiaries.

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