Exploring Lean & Green Internet of Things (IOT) Wireless Sensors Framework for the Adoption of Precision Agriculture Practices Among Indiana Row-Crop Producers
Abstract
The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (Nitrogen & Phosphorus), farm machinery fuel costs and decrease in labor productivity leading to non-optimized usage of farm-inputs. A structured literature review describes Lean and Green practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of Precision Agriculture practices (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm-machinery navigation). The literature review describes low adoption of Internet of Things (IoT) based precision agriculture practices, such as variable rate fertilizer (39 %), variable rate pesticide (8%), variable rate irrigation (4 %), cloud-based data analytics (21 %) and telematics (10 %) amongst Midwestern row crop producers. Barriers for the adoption of IoT based Precision Agriculture practices include cost effectiveness, power requirements, communication range, data latency, data scalability, data storage, data processing and data interoperability. Focused group interviews (n=3) with Subject Matter Expertise (SME’s) (N=18) in IoT based Precision Agriculture practices were conducted to understand and define decision-making variables related to barriers. The content analysis and subsequent ISM model informed an action research approach in the deployment of an IoT wireless sensor nodes for performance improvement. The improvements resulted in variable cost reduction by 94 %, power consumption cost reduction by 60 %, and improved data interoperable and userinteractive IoT wireless sensor-based data pipeline for improved adoption of Precision Agriculture practices. A relationship analysis of performance data (n=2505) from the IoT sensor deployment empirically validated the ISM model and explained the variation in power consumption for mitigation of IoT adoption among producers. The scope of future research for predicting IoT power consumption, based upon the growing season through correlation was developed in this study. The implications of this research inform adopters (row-crop producers), researchers and precision agriculture practitioners that a Lean and Green framework is driven substantively by cost and power concerns in an IoT sensors-based precision agriculture solution.
Degree
Ph.D.
Advisors
Laux, Purdue University.
Subject Area
Agriculture|Agronomy|Energy|Industrial engineering|Information Technology|Plant Pathology|Sustainability|Web Studies
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