Whitepapers

LoRa Alliance ® and LoRaWAN ® are registered trademarks. Used with permission. ©2020 LoRa Alliance ® www.lora-alliance.org WHY LORAWAN ® IS THE LOGICAL CHOICE FOR CROP PRODUCTION MONITORING SOLUTIONS AMBIENT MONITORING FOR GREENHOUSES Crop yields for tomatoes or flowers, for example, in greenhou ses are highly reliant on the amb ient conditions in the growing env ironment. n addition to irrigation and ertiliz ation, these comprise the air hu midity temperatu re, and 2 lev els. olu tion prov iders in this area inclu de Nemeu s, NK Watteco, Ursalink, and mcf . PEST DETECTION AND POLLINATION IN ORCHARDS With the US ood and Agricu ltu re rganiz ation AO estimating that 20–40% of global crop yields are lost annu ally du e to pests and diseases the need to control pests is clear. owev er, ov er-u se of pesticides can contaminate ecosy stems and ltimately resu lt in pesticides ecoming less ef ectiv e. raditional arming relies on traditional calendar-b ased pest control procedu res that se pesticides regardless of whether pests hav e een detected. Wirelessly -connected intelligent dev ices are radically transf orming this area of agricu ltu re enab ling real-time monitoring, modeling of pest mov ements and disease orecasting, thereb enab ling armers to pinpoint when and how mu ch pesticide to se. owev er, there are good pests and ad pests and some armers, notab ly wine growers, orchard produ cers, and almond armers, req ire their plants to e pollinated. n act, according to the AO approx imately of crops that produ ce ru it or seeds or hu man consu mption depend on pollinating insects, such as bees or flies . armers, theref ore, need to e ab le to ensu re the nu mb er of pollinators on their land is sufficient to pollinate their trees and enab le them to achiev e high ields. Wirelessly connected dev ices of er the ab ility to do this monitoring ee activ ity at hiv es, or ex ample. ther ty pes of sensors can count the number of moths of flies in insect traps. DATA MANAGEMENT AND LEVERAGING INSIGHTS his paper has introdu ced ex amples and solu tions rom across the entire landscape of precision agricu ltu re, demonstrating the readth and scale of the opportu nities that are locked-u p in arming-related data. owev er, many of the cu rrent solu tions, ased on satellite and drone images, su er rom eing retrospectiv e in that they merely report what has happened and this reporting is sed to add to records and pu t in place strategies to av oid the same issu es recu rring in tu re ears. olu tions are siloed so data gathered rom a water management solu tion isn' t integrated with data rom a temperatu re or ertiliz ation solu tion. he only way to integrate this dif erent data is engaging the armer' s knowledge. Aggregation is needed to ring together all the data points and do so in a timely way so insights can e acted on immediately . Rememb er, armers will not e ab le to sit in their tractors and cy cle throu gh six or eight dif erent applications to access this hou r' s data, then analy e it and decide to decrease irrigation and increase ertiliz ation, or ex ample. hese indiv idu al data points only ecome sef l once they are integrated and pu t into the entire contex t, along with historical data rom prev iou s ears. Applications that can aggregate these disparate data, ideally into a simple graphical ser interf ace on a mob ile dev ice, will enab le armers to ev alu ate all the data and take action in the moment, thereb enab ling greater crop ields and more efficient usage of resources such as water and ertiliz er. here is space in the market or serv ice prov iders who can inv est in monitoring eq ipment to e deploy ed at arms and then deriv e alu e rom the data and insights they collect to armers as a serv ice. or ex ample, a co-operativ e of 00 potato armers cou ld deploy 00 weather stations, thereb cov ering the co-operativ e' s entire territory to get etter inf ormation on the temperatu re and hu midity and analy e the risk of potato light with greater granu larity . ch a serv ice wou ld enab le armers to estimate the ty pe or antity of produ cts to e sed on the dif erent areas of their arms and, in the ev ent of an ou tb reak of disease, allow them to alert their neighb ors. n this ex ample, ey ond connectiv ity the co-operativ e can se the high- perf ormance algorithm to analy e the data collected and cross-compare all the inf ormation rom weather stations and air temperatu re and hu midity sensors to create a real prediction serv ice or disease propagation. his prov ides a lev el of data integration that was prev iou sly not possib le as now the same inf ormation arriv es on the armers' cellphones. The co-operative also benefits from the data collected so it can manage its stock of treatment produ cts and improv e the accu racy of the produ ction orecast ef ore harv est time.

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