The Benefits of Precision Agriculture for Mitigating Pest Infestations
When it comes to pest management, the opportunity cost of forgoing precision agriculture is simply too high. On a global scale, pathogens and pests are reducing crop yields for five major food crops by 10 percent to 40 percent. In addition to vast economic consequences, damages from pests also often have a negative ripple effects into the environment, including the potential loss of wildlife habitats, and obstruction of streams and waterways. Effective pest management relies on the use of tools and strategies made possible by access to Big Data. Digital agriculture enables data-driven decision-making that helps integrate pest management into the day-to-day management of farms.
Pest management is when farm management utilizes environmentally sensitive prevention, avoidance, monitoring, and suppression strategies to manage weeds, insects, diseases, animals, and other organisms that either directly or indirectly cause damage or annoyance. There are a plethora of factors and farmers employ a range of pest management strategies including tilling soil, rotating crops, scouting fields, carefully considering factors such as plant density and planting dates, and often applying organic or synthetic pesticides.
SaraniaSat’s™ digital agriculture systems uses combined hardware and software remote-sensing solutions to gather data more frequently and accurately. When measurements from Long Wave Infrared (LWIR) remote-sensing imagery are processed with SaraniaSat’s™ nonlinear, weak-signal detection algorithms, the resulting data proves to be an effective predictor of crops stress and ultimate harvest yield. In addition to LWIR, Mid-Wave Infrared (MWIR) imagery also contains actionable, crop-stress information that is absent in conventional Normalized Difference Vegetation Index (NDVI) that is currently, widely used for precision agriculture.
The spectral ranges differ between LWIR (8-10 micron wavelength band) and MWIR (3-5 micron wavelength band), and so do a few other factors. For example, the earth emission component in the MWIR band is significantly lower than in the LWIR band, and mixed with solar reflectance, making the accurate determination of canopy and soil temperatures difficult. Both processed MWIR and LWIR imagery are superior to NDVI maps in their ability to detect soil moisture variations in pre-planting bare soil. Also, processed LWIR imagery is more sensitive in displaying finer variations over a broader dynamic range of crop stress.
The data SaraniaSat™ processes is often combined with external sources such as weather information, subject to analysis and interpretation by expert agronomists. The resulting actionable, farm management information enables farmers to make more informed and appropriate decisions, quickly and accurately, saving time and precious resources such as labor, fertilizer, irrigation and crop protection costs. From a farm management perspective, ‘early warning’, or knowledge of the crop health prior to the visible manifestation of the effects of crop stress, is of great value.
Pests, diseases, and weeds can result in costly and irreparable harm to livestock and crops. Integrated pest management aims at solving pest problems while simultaneously minimizing the risks to people and the environment. It is an ecosystem-based strategy that focuses on early detection and mitigation of pest infestation and their damage through a combination of techniques including biological control, habitat manipulation, modification of farming practices, and the use of resistant varieties. The idea is to use the actionable information provided by SaraniaSat™ to maximize the benefits of farming practices that promote plant health and allow crops to escape or tolerate pest injury, and to enhance the impact of any beneficial, natural controls already present.
Left unchecked, pests, invasive species, and plant diseases have the potential to devastate entire agricultural industries, eliminating jobs, threatening our food supplies and costing billions. Luckily, SaraniaSat’s™ remote-sensing data acquisition and analysis solutions provide an efficient and cost-effective way to gather data not only at the individual farm or field scale, but also enabling accurate aggregation of data over regional and national scales. SaraniaSat’s™ non-linear, weak-signal detection and sensor fusion algorithms operate efficiently on the “edge,” in other words, at the point at which the digital data is acquired and therefore reduces the time delay or latency of providing our customers with actionable information to within hours of acquisition of raw data. This enables accurate data to reach farmers faster than ever.
Digital farming is poised to bring about an unprecedented revolution in agriculture by illuminating, predicting, and significantly influencing the continuum of cultivation issues across the modern farm. With the progression of predictive algorithms, analytics, and cutting-edge technologies, modern agriculture is pursuing ways to precisely and scientifically deal with harmful pests and plant diseases.
Agriculture remains one of the most complex business sectors that can be impacted by Big Data, and SaraniaSat™ is here to help. For more information, contact SaraniaSat™ here.
 T. George, S. Gulati, S. Martin and S. Nozaki, “Comparison of Mid Wave Infrared (MWIR) and Long Wave Infrared (LWIR) Imagery for Precision Agriculture Applications,” Proceedings Paper, 2019 IEEE AEROSPACE CONFERENCE; Big Sky, MT; MAR 02-09, 2019; ISBN:978-1-5386-6854-2.