2022 Spring Farm Outlook

Page 30 Spring 2022 Logan County Farm Outlook LINCOLN DAILY NEWS March / April 2022 Spring 2022 Logan County Farm Outlook LINCOLN DAILY NEWS March / April 2022 Page 31 nor scalable to measure leaf nitrogen by hand throughout the course of a season. In a first-of-its-kind study, a University of Illinois research team put hyperspectral sensors on planes to quickly and accurately detect nitrogen status and photosynthetic capacity in corn. “Field nitrogen measurements are very time- and labor-consuming, but the airplane hyperspectral sensing technique allows us to scan the fields very fast, at a few seconds per acre. It also provides much higher spectral and spatial resolution than similar studies using satellite imagery,” says Sheng Wang, research assistant professor in the Agroecosystem Sustainability Center (ASC) and the Department of Natural Resources and Environmental Sciences (NRES) at U of I. Wang is lead author on the study. The plane, fitted with a top-of-the-line sensor capable of detecting wavelengths in the visible and near infrared spectrum (400-2400 nanometers), flew over an experimental field in Illinois three times during the 2019 growing season. The researchers also took in-field leaf and canopy measurements as ground-truth data for comparison with sensor data. The flights detected leaf and canopy nitrogen characteristics, including several related to photosynthetic capacity and grain yield, with up to 85% accuracy. Wang explains, “Under our approach, we can detect the nitrogen status of the crop and make some real-time adjustments for the agricultural stakeholders. MRTN provides recommended nitrogen fertilization rates based on the economic tradeoff between soil nitrogen Continue 8 fertilizer rates and end-of-season yield. Our remote-sensing approach can feed plant nutrient status into the MRTN system, enabling real-time crop nitrogen management. It can potentially shift the current recommendations based on pre-growing season, soil-centric fertilization to a diagnosis based on real-time plant nutrition, improving agroecosystem nitrogen use efficiency.*2” In addition to applying needed nitrogen to the plants, this technology helps keep nitrogen out of runoff water that travels down our streams and rivers and ultimately contributes to dead zone areas in the oceans. “Essentially, you can’t manage what you can’t measure. That is why we put so much effort into this technology.*2” 2. Internet connected sensors “Monitoring of the crop field in conventional farming requires intensive labor, physical equipment, time, and effort. IoT (the Internet of Things) technology provides an alternative to these traditional methods. An IoT device contains one or more sensors that collect data and provide accurate information via mobile applications or other means in real-time. These sensors perform countless activities, such as soil, temperature & humidity sensing, plant & livestock tracking, and more. It also facilitates remote monitoring of farms, providing greater convenience to farmers. Further, new irrigation systems utilize IoT sensors for automation of the delivery of water to crops. These constitute evapotranspiration sensors, on-site soil moisture sensors, rain sensors, and several others. Startups are developing innovative sensor solutions that combine IoT technology with drones, robots, and computer imaging to increase the agility, accuracy, and precision of farm processes. These send on-time alerts and improve the response time for areas that need attention.*3” All of these sensors are connected to hubs which coordinate the incoming data via various types of internet connections, including fixed and mobile wireless, WIFI, DSL, cable and fiber optic systems. The growth of the wireless internet industry, for the most part, makes all of this possible. Expect this type of automation to grow and influence agriculture greatly in the future. 3. Artificial intelligence Having various and many internet connected sensors in an agricultural setting provides a great deal of data which if it were not for developing Artificial Intelligence, would add to instead of relieving a farmer’s work load. AI gathers, collates, corroborates and interprets collected sensor and internet data to give the farmer an interpreted picture rather than a mass of loose uninterpreted, dissimilar and unrelated data. “Incorporating artificial intelligence in agriculture provides farmers with real-time Continue 8