Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / With the advance the adoption of precision agriculture in Brazil, growing use the technological tools georeferenced soil sampling and crop yield. Relations and crossing data information between these two tools can assist in improving the management of crops through specific recommendations of each element with reduced costs, efficient use of inputs, increasing productivity and profitability of crops. The objective of this study was to evaluate the influence of soil chemical properties on grain yield of soybean and corn in two experimental fields southern Brazil through principal component analysis and hierarchical cluster. The research happened in two experimental areas of 132 and 124 hectares in typical Oxisol. The soil samples were taken with a grid sampling of one hectare in the 0-0.10 m layer. The yields of the cultures were related at the same point each soil analysis using the annual average productivity at a radius of 25 m around each sampling point. At the first experimental area of 132 hectares, were investigated yield maps soybean harvest crop 2008/09; soybean crop 2009/10; soybean crop 2010/11 and corn crop 2011/12. In the second study area of 124 hectares, were investigated yield maps soybean harvest crop 2009/10, corn harvest 2010/11 and soybean crop 2011/12. The selection of yield maps followed the criterion to evaluate crops with less than two years of the completion of soil sampling, where only occurred fertilization of crop maintenance. Both areas had high fertility, the first area to saturation (V) average 65.16% and 83.03% in the second area. At the first experimental area the increase in productivity is being explained by the increase of the attributes: saturation of calcium and magnesium in the cation exchange capacity (Sat Ca, Mg Sat), V, and the calcium: magnesium (Ca: Mg). In the second experimental area the increase attribute chemical Sat Mg committed crop yields. In both experiments the elevation of attributes Sat Ca, V and Ca: Mg contributed to raising crop yields. The balance of cations in the exchange complex was the chemical attributes that best explained the variability in crop investigated. / Com o avanço da adoção da agricultura de precisão no Brasil, cresce a utilização de ferramentas tecnológicas de amostragem de solo georreferenciadas e mapas de produtividade das culturas. As relações e o cruzamento de dados entre as informações destas duas ferramentas podem auxiliar no aprimoramento do manejo das lavouras através de indicações específicas de cada elemento com redução de custos, uso eficiente de insumos, aumento da produtividade e rentabilidade das culturas. O objetivo desse trabalho foi avaliar a influência de atributos químicos do solo na produtividade das culturas de soja e milho em duas áreas comerciais no planalto do RS, através da análise de componentes principais e o agrupamento hierárquico - cluster. A investigação ocorreu em duas áreas experimentais de 132 e 124 ha em Latossolo Vermelho distrófico típico. As amostragens de solo foram realizadas baseadas em uma malha regular de 100 metros por 100 metros na camada de 0-0,10 m. As produtividades das culturas foram relacionadas no mesmo ponto de cada análise de solo utilizando-se a média de produtividade anual em um raio de 25 m ao redor de cada ponto amostral. Na primeira área experimental de 132 ha, foram investigados os mapas de colheita de soja-safra 2008/09; soja-safra 2009/10; soja-safra 2010/11 e milho-safra 2011/12. Na segunda área investigada, de 124 ha, foram estudados os mapas de colheita de soja-safra 2009/10; milho-safra 2010/11 e soja-safra 2011/12. A escolha dos eventos de colheita analisados seguiu o critério de avaliar safras com período inferior a dois anos da realização da amostragem de solo, em que apenas ocorreram as adubações de manutenção das culturas. Ambas as áreas apresentavam fertilidade elevada, sendo a primeira com a saturação de bases (V) média de 65,16% e a segunda com 83,03%. Na primeira área experimental, o acréscimo de produtividade está sendo explicado pela elevação dos atributos: saturação de cálcio e de magnésio na capacidade de troca de cátions (Sat Ca; Sat Mg), V e relação cálcio: magnésio (Ca:Mg). Na segunda área experimental, a elevação do atributo químico Sat Mg comprometeu a produtividade das culturas. Em ambos os experimentos a elevação dos atributos Sat Ca, V e Ca: Mg contribuíram para elevar a produtividade das culturas. O equilíbrio de cátions no complexo de troca foi o atributo químico que melhor explicou a variabilidade da produtividade das culturas investigadas.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Soil samples from sampling grids is currently the leading precision farming tool used in Brazil. But it still has doubts about the economic feasibility of using grids with high density points. New technologies such as the use of management zones (MZ) to guide soil samples have been studied in order to reduce the number of samples and consequently the sampling costs while maintaining the quality of the information obtained. However, this method lacks scientific information about its efficiency. The aim of this study was to evaluate the technical efficiency (chapter I) mapping chemical attributes of soil and economic viability (chapter II) of the recommendations of fertilizers and from the parameters obtained by conventional sampling, sample grids and MZ delimited by different methodologies. The study was carried out in Boa Vista Missões - RS, on an area of 118,9 ha. The soil sampling was carried out in 2015, using a sample grid of 0.5 ha. From the systematic elimination of sample points simulated the use grids; 1, 2, 3 and 4.5 ha. The MZ were defined based on data altimetry of the area and the yield maps. To Chapter I used the data of P, K and pH which were submitted to descriptive statistical analysis. Data collected in sampling grid was submitted to geostatistical analysis. The accuracy of thematic maps was evaluated from the Pearson correlation and coefficient of relative deviation. For chapter II we used the P, K and SMP index to generate recommendations of P2O5, K2O and lime, respectively. The recommendation of errors were calculated in relation to the reference grid (0.5 ha) in application area (ha) kg of fertilizer and in real (R$), later smpling costs were compared with the total cost of errors recommendation. The increase in the size of the sampling grid resulted in loss of information on the spatial variability of attributes and concenquentemente raised the deviations in both fertilizer and corrective recommendations. For a proper assessment of the spatial variability of soil chemical attributes and an accurate prescription of inputs recommends the use of sampling grid of 0.5 ha. / A amostragem de solo a partir de grades amostrais é atualmente a principal ferramenta de agricultura de precisão utilizada no Brasil. Porém, ainda se tem dúvidas sobre a viabilidade econômica da utilização de grades com alta densidade de pontos. Novas tecnologias como o uso de zonas de manejo (ZM) para orientar amostragens de solo têm sido estudadas, visando reduzir o número de amostras e consequentemente os custos de amostragem, mantendo a qualidade das informações obtidas. Porém, tal metodologia carece de informações científicas sobre a sua eficiência. Objetivou-se com este estudo avaliar a eficiência técnica (capitulo I) do mapeamento dos atributos químicos do solo e a viabilidade econômica (capitulo II) das recomendações de fertilizantes e corretivos a partir dos parâmetros obtidos através da amostragem convencional, grades amostrais e ZM delimitadas por distintas metodologias. O estudo foi realizado em Boa Vista das Missões - RS, em uma área de 118,9 ha. A amostragem de solo foi realizada em 2015 através de uma grade amostral de 0,5 ha. A partir da eliminação sistemática de pontos amostrais, simulou-se a utilização de grades de 1, 2, 3 e 4,5 ha. As ZM foram delimitadas com base em dados altimétricos da área e de mapas de produtividade. Para o capítulo I utilizou-se os dados de P, K e pH os quais foram submetidos à análise estatística descritiva. Os dados coletados em grade amostrais foram submetidos à analise geoestatística. A acurácia dos mapas temáticos foi avaliada a partir da correlação linear de Pearson e coeficiente de desvio relativo. Para o capitulo II utilizou-se os teores de P, K e índice SMP para gerar as recomendações de P2O5, K2O e calcário, respectivamente. Calcularam-se os erros de recomendação em relação à grade referencia (0,5 ha) em área de aplicação (ha), kg de fertilizante e em reais (R$). Posteriormente os custos de amostragem foram comparados com os custos totais de erros de recomendação. O aumento da dimensão da grade amostral resultou em perdas de informações sobre a variabilidade espacial dos atributos e consenquentemente elevou os desvios nas recomendações tanto de fertilizantes quanto de corretivo. Para uma adequada aferição da variabilidade espacial dos atributos químicos do solo e uma acurada prescrição de insumos recomenda-se a utilização de grade amostral de 0,5 ha.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work is to present and discuss the Precision Farming in a simple way through the routines of the precision farming into the system Campeiro CR-7 and its performance in order to analyze the spatial variability of soil properties and productivity. Chapter I The Loop Sampling, provides a tutorial on developing a sampling plan for spatial assessment of physic-chemical properties of soil by means of geostatistical techniques, the sampling plan can contribute significantly to reducing the cost of collection of samples as well as quality of these samples. Chapter II Map of Fertility analyzes a tool that assists the user in the classification of levels of soil fertility, and the result presented in map form, where the final map is a cross between a map that contains phosphorus in the soil and map showing the clay making the recommendation to apply phosphorus fertilizer supplier to more precise areas where there is more than one class of clay. Chapter III Productivity Map filtering is performed on the data collection, making it possible to make a map of productivity consistent with the reality of the field with a greater reliability of the data, providing information relevant to agricultural management. / Este trabalho consiste em apresentar e discutir a Agricultura de Precisão de forma simples, através das rotinas de Agricultura de Precisão do Sistema CR-Campeiro 7, conjunto de programas elaborado no Departamento de Engenharia Rural da Universidade Federal de Santa Maria UFSM, Laboratório de Geomática, concebido e programado pelo Prof. Dr. Enio Giotto, e o desempenho desses programas, visando à análise da variabilidade espacial de propriedades químicas do solo e da produtividade. O Capítulo I Malha de Amostragem traz um tutorial sobre a elaboração de um plano de amostragem para avaliação espacial de propriedades físico-químicas de solo por meio de técnicas geoestatísticas; o plano de amostragem pode contribuir de forma relevante para diminuição do custo de coleta de amostras como também para a qualidade dessas amostras. O Capítulo II Mapa de Fertilidade analisa uma ferramenta que auxilia o usuário na classificação dos níveis de fertilidade do solo, sendo o resultado apresentado em forma cartográfica, onde o mapa final é a integração temática entre o mapa que contem os teores de Fósforo no solo e o mapa que apresenta os teores de Argila, tornando a recomendação para aplicação de fertilizantes fornecedores de Fósforo mais precisas para áreas onde ocorre mais de uma classe de Argila. No Capítulo III Mapa de Produtividade é realizada uma filtragem nos dados de colheita, tornando possível a confecção de um mapa de produtividade coerente com a realidade do campo, com uma confiabilidade maior dos dados, fornecendo informações relevantes ao gerenciamento agrícola.

Orientador: José Antonio Siqueira Dias / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T06:29:21Z (GMT). No. of bitstreams: 1 Roque_Wellington_D.pdf: 36135434 bytes, checksum: 2e9ca0be59b1286da36d45ebc67dfd0d (MD5) Previous issue date: 2012 / Resumo: A aplicação de sensores de umidade do solo vem aumentando devido à necessidade do uso eficiente da água para irrigação. Uma das técnicas amplamente utilizadas para o desenvolvimento desses sensores é a de dissipação de calor. Nesta técnica, um elemento aquecedor é usado para aplicar um pulso de calor no solo, gerando um aumento de temperatura, o qual é mensurado e relacionado com a umidade do solo. Porém, um dos principais problemas desse tipo de sensor é sua baixa sensibilidade. Buscando resolver esse problema, foi desenvolvido um sensor que utiliza um transistor bipolar como único elemento aquecedor e transdutor de temperatura. Uma energia aplicada causa aquecimento no transistor e consequentemente provoca uma variação na tensão da junção base-emissor. Com isso, realizamos um estudo, a fim de determinar qual o valor ideal de energia aplicada que proporcionaria a máxima sensibilidade na medida da variação da temperatura. Para isso foram utilizados para fabricação dos sensores 16 transistores com encapsulamento metálico (TO - 18). Os sensores foram inseridos em amostras de solo deformadas, que foram saturadas para realização da medida da umidade do solo, através do método gravimétrico. Foram realizados com cada sensor curvas de calibração relacionando a umidade do solo com a com o valor de ?T, para os diferentes valores de energias fornecidos no sensor. Após análise dos dados, foram escolhidos dois sensores que apresentaram maior variação entre suas curvas de calibração para serem inseridos em uma solução de Agar Agar e no Ar, realizando várias medidas nos sensores, sempre com os mesmos valores de energia, para verificar a repetibilidade dos mesmos. Os resultados mostraram que, em relação à energia aplicada no sensor, àquela que proporcionou uma maior sensibilidade em função da umidade do solo foi de 1:5J (25 V, 6,3 mA, 10 s). Foi constatado que a sensibilidade do sensor melhora com o aumento da potência, sendo que esta melhora é relativamente independente da energia fornecida. Em relação às medidas realizadas no Agar Agar, observamos uma melhora considerável do erro em função das medidas realizadas no solo, de 10% para o solo, para 4% no Agar Agar. Para as medidas realizadas no Ar, o valor do erro diminui para 1%. Para as medidas realizadas no solo, na solução de Agar Agar e no Ar, chegamos à conclusão que a diminuição do erro das medidas realizadas em cada um deles, ocorre devido à mudança do contato do sensor com o solo durante as medidas, visto que o solo, quando começa perder umidade ele altera o seu volume, levando a uma diminuição da superfície de contato do mesmo com o sensor. Esse erro pode ser também em função da variação da densidade do solo que ocorre durante a realização dos ensaios, causando uma diferença no valor das medidas, já citadas em outras literaturas. Observamos também que para a melhor utilização do sensor em diferentes faixas de umidade do solo, há a necessidade de alterar a energia aplicada, devido à sensibilidade de o sensor ser maior para umidades mais baixas (abaixo de 30%), necessitando de um acréscimo do valor da energia aplicada para se obter uma melhor sensibilidade para umidades altas (acima de 30%) / Abstract: The use of soil moisture sensors is increasing due to the need for efficient use of water for irrigation. One technique widely used for the development of these sensors is the heat dissipation. In this technique, a heater element is used for applying a pulse of heat into the soil, causing a rise in temperature, which is measured and related to the soil moisture. However, a major problem of this type of sensor is its low sensitivity. To solve this problem, we developed a sensor that uses a bipolar transistor as a single element heater and temperature transducer. Energy applied to the transistor causes its heating, which in turn causes a variation on the voltage of the base-emitter junction. So we conducted a study to determine the optimal value of energy applied to provide maximum sensitivity in the measurement of temperature variation. To do this, 16 metal case TO-18 package transistors were used in the fabrication of the sensors. The sensors were inserted in deformed soil samples, which were water-saturated to perform the measurement of soil moisture using the gravimetric method. With each sensor we obtained calibration curves relating the soil moisture to the value of temperature for the different values of energy supplied to the sensor. After analyzing the data, two sensors that showed greater temperature variation in their calibration curves were chosen to be inserted into a solution of Agar Agar and in the air, in order to perform multiple measurements with the sensors at the same energy values to check their repeatability. The results showed that the energy which provided a greater sensitivity as a function of soil moisture was 1,5 J (25 V, 6,3 mA 10 s). We have found that the sensor sensitivity improves with increasing dissipated power and this improvement is relatively independent of the energy supplied. Regarding the measurements performed in Agar Agar, we observed a significantly lower error compared to the measurements taken in the soil: from 10% for the soil measurements to 4% in the Agar Agar. For the measurements made in air, the error decreased to 1%. For the measurements made in soil, in solution of Agar Agar and in air, we concluded that the decrease of the measurements errors performed in each of them occurs due to a changing in the contact between the sensor and the media during the measurements, because the soil changes its volume when it begins to lose moisture, leading to a decreased surface contact with the sensor. This error can also be due to the variation of soil density that occurs during the tests. We also observed that for the best use of this sensor in different ranges of soil moisture there is a need to change the applied power due to the higher sensitivity of the sensor for moisture content below 30%, needing an increase in the amount of this power to obtain a better sensitivity to humidity above 30% / Doutorado / Eletrônica, Microeletrônica e Optoeletrônica / Doutor em Engenharia Elétrica

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Grâce aux technologies de l’information et aux systèmes de positionnement par satellites (GNSS), l’agriculture de précision raffine l’échelle d’observation et d’intervention, du champ à la plante individuelle. La télédétection, notamment à travers les images satellitaires, a également permis de surveiller l’évolution de la culture, avec une fréquence temporelle et une résolution spatiale accrues. La vigueur de la culture est un paramètre crucial de la fertilisation, permettant d’optimiser l’apport en intrants et de générer des retombées économiques et environnementales. Plusieurs manières d’estimer la vigueur sont possibles. Pour une culture en rangs comme le maïs (Zea Mays L.), nous avons privilégié la biomasse qui servira de proxy à la vigueur. Notre étude s’appuie sur l’hypothèse que l’avènement des drones agricoles ouvrira la voie à une meilleure estimation de la vigueur. Nous supposons que la flexibilité de la plateforme combinée avec la très haute résolution spatiale (THRS) des images droniques permettra un suivi plus précis de l’évolution de la vigueur, tant dans l’espace que dans le temps – conditions critiques pour le déploiement de l’agriculture de précision. Les recherches des 15 dernières années s’appuient sur le drone pour collecter des images THRS, dont le traitement fait appel principalement à la méthode des indices de végétation, tel le NDVI. Plusieurs questions se posent : – Le recours au NDVI est-il la manière la plus efficiente d’exploiter la THRS des images droniques ? Si non, comment exploiter autrement et mieux cette imagerie ? – Attendu les contraintes de temps, la quantité de données et les impératifs logistiques, la méthode de traitement adoptée sera-t-elle suffisamment robuste ? Les concepts utilisés seront-ils simples et compréhensibles pour obtenir l’adhésion des utilisateurs finaux, c’est-à-dire les agriculteurs ? – Les capteurs à bord des drones pouvant être orientés assez librement, quels sont les effets des angles de visée sur les résultats ? D’abord, nous avons évalué la pertinence de l’approche classique, consistant à utiliser l’indice NDVI pour déterminer les paramètres de la culture. Pour cela, la plateforme dronique est comparée avec le capteur de proximité GreenSeeker. Il en découle que le drone se montre moins performant que le GreenSeeker pour l’estimation de la biomasse du maïs. Nous avons ainsi délaissé l’approche classique et opté pour un indicateur de surface apparente, extrait à l’aide des techniques de vision par ordinateur appliquées à des images RGB. Cette méthode tire profit de la THRS offerte par l’imagerie dronique et produit un proxy robuste de la biomasse, au niveau surfacique (par mètre carré) et linéaire (par rang). Nous avons aussi constaté que les résultats n’étaient pas affectés par les angles d’acquisition des images (au nadir et obliques). Partant, cette recherche ouvre la perspective à des applications de la dronautique en agriculture de précision, pour l’estimation de la vigueur et d’autres paramètres fondamentaux entrant dans les algorithmes d’optimisation des intrants. Elle offre également la possibilité d’imaginer des plateformes non-droniques pour l’acquisition des images THRS dans le contexte de la fertilisation. / Thanks to information technologies and GNSS (Global Navigation Satellite System), precision agriculture is refining the scale of observation and intervention, from the field to the individual plant. Remote sensing, in particular through satellite imagery, has also made it possible to monitor the crop dynamics, with increasing time frequency and spatial resolution. Crop vigor is a crucial parameter allowing the optimization of inputs, and consequently economic and environmental benefits. Several ways to estimate crop vigor are possible. For a row crop such as corn (Zea Mays L.), aboveground biomass has been favored and will serve as a proxy for vigor. Our study is based on the hypothesis that the recent advent of agricultural UAVs (Unmanned Aerial Vehicles) will pave the way for a better estimation of crop vigor. We assume that the flexibility of the UAV combined with the very high spatial resolution of their images will allow a more accurate monitoring of crop vigor, both in space and time – conditions which are critical for the deployment of precision agriculture. Research over the past 15 years has relied on the UAV to collect images of very high spatial resolution. However, the processing of those images is mainly based on vegetation indices, especially the NDVI. Several questions arise from the above observations: – Is the use of NDVI the most efficient way to exploit the spatial resolution of UAV images? If not, is there a better way to exploit this imagery? – How to respond to time and logistical constraints in the image processing so that it is ultimately adopted by farmers? – Since UAV-borne sensors can be oriented quite freely, what are the effects of the acquisition angles on the results? First, we assessed the relevance of the traditional approach which uses the NDVI index to determine crop parameters. To do that, we compared the UAV platform with the GreenSeeker proximal sensor. The results showed that the UAV is less efficient than the GreenSeeker in estimating corn biomass. We thus moved away from the traditional approach and opted for a method extracting the apparent leaf area, through computer vision techniques applied to RGB images. This method took advantage of the very high spatial resolution offered by UAV images and produced a robust proxy for corn biomass, at the surface (per square meter) and row levels. We also found that the results were not affected by the acquisition angles of the images (nadir and oblique). Therefore, this research opens the perspective to UAV applications in precision agriculture, for the estimation of vigor and other fundamental parameters used in input optimization algorithms. It also offers the possibility to imagine non-UAV based platforms for the acquisition of very high resolution images in the context of fertilization.

Die kontinuierliche Überwachung von Pflanzenparametern spielt eine wichtige Rolle in der Präzisionslandwirtschaft. Als in situ Monitoring-Systeme erscheinen drahtlose Sensornetzwerke (engl. Wireless Sensor Networks (WSNs)) geeignet, um den Zustand von Kulturpflanzen zu erfassen und diesen in stets aktuelle Parameterkarten zu transformieren. Derartige Karten können potenzielle wachstums- und ertragsmindernde Faktoren frühzeitig identifizieren und Entscheidungshilfen geben, die zu einer ortsdifferenzierten, zielgerichteten und nachhaltigen Bewirtschaftung landwirtschaftlicher Produktionsflächen beitragen. Die vorliegende kumulative Dissertation beschäftigt sich in diesem Zusammenhang mit der automatisierten und kosteneffizienten in situ Erfassung eines wichtigen Pflanzenparameters, dem sogenannten Blattflächenindex (engl. Leaf Area Index (LAI)). Mittels handelsüblicher WSN-Hardware wird zunächst ein kostengünstiger Sensor-Prototyp für eine passive, transmissionsbasierte LAI-Erfassung konzipiert und, begleitet durch Feldkampagnen, experimentell weiterentwickelt. Im Verlauf der Arbeit wird eine auf die spezielle Anwendung zugeschnittene Netzwerkarchitektur entworfen, die den Prototypen in ein ganzheitliches Langzeit-Monitoring-System überführt. Durch exemplarisch realisierte Deployments an zwei unterschiedlichen Standorten und den daraus erfassten empirischen Datensätzen wird das Potenzial drahtloser Sensornetzwerke für eine kontinuierliche und zeitlich hochauflösende LAI-Erfassung analysiert. Dabei werden effektive Methoden zur Prozessierung und Filterung von in situ Sensordaten entwickelt und untersucht, inwieweit diese die Qualität der abgeleiteten LAI-Schätzung verbessern. Ein Schwerpunkt der empirischen Potenzialanalyse liegt dabei auf der differenzierten Erfassung von sortenspezifischen und trockenstressbedingten Veränderungen. Weiterhin wird der Einfluss von Umwelt und Vegetation auf die Qualität drahtloser Verbindungen in landwirtschaftlichen WSN-Deployments betrachtet. Basierend auf dem empirischen Datensatz wird gezeigt, dass das Pflanzenwachstum die Qualität exemplarischer Verbindungen beeinträchtigt. Aus dieser Beobachtung wird ein Modell für ein signalstärkebasiertes Pflanzen-Monitoring abgeleitet und die generelle Machbarkeit dieses neuartigen Ansatzes untersucht.

The United States Farm Bill is a multi-billion-dollar federal legislation reenacted every five years to provide funding towards crop protection, nutrition, environmental protection, and other important focuses. The largest conservation program within the Farm Bill is the Conservation Reserve Program, which encompasses many conservation practices such as CP-33: habitat buffers for upland birds. Conservation implementation through the Farm Bill relies on voluntary producer enrollment in exchange for a rental fee to not farm enrolled land for a set time. I used yield data collected across six years from 36 agricultural fields in Humphreys and Holmes counties, Mississippi, USA, and a range of commodity prices to compare the change in economic and environmental opportunities available through economically targeted conservation enrollment between the 2014 Farm Bill and the 2018 Farm Bill. I found the 2014 Farm Bill was consistently higher in economic revenue and conservation opportunity compared to the 2018 Farm Bill

<p>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). </p> <p>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 user-interactive 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. </p> <p>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. </p>

The purpose of this research is to design, develop, evaluate, and demonstrate an open source gateway and mobile application for the SmartFarm open source decision support system to improve agricultural stewardship, environmental conservation, and provide farmers with a system that they own. There are very limited options for an open source gateway for collecting data on the farm. The options available are: expensive, require professional maintenance, are not portable between systems, improvements are made only by the manufacturer, limited in customization options, difficult to operate, and data is owned by the company rather than the farmer. The gateway is designed to send data to the cloud from remote SmartFarm Data Acquisition (DAQ) nodes, collect measurement data from remote SmartFarm DAQ nodes, provide a means of wirelessly programming remote SmartFarm DAQ nodes, and a tool that provides data analysis and insight from remote SmartFarm DAQ nodes. It is evaluated to work with 900MHz radios, SmartFarm DAQ nodes, and costs $35. Its setup takes 4 steps and ~20 minutes installation time, does not require maintenance, can utilize Wi-Fi, Bluetooth, and other wireless protocols, and software can port to other systems. The gateway measured data rate of 93.4Mbit/s internet upload speed, passing a range of 252 to 1592 bytes of data from a remote node to the cloud, consumes 2.8 Watts, with a software efficiency of 25% CPU usage, a measurement efficiency of 1 message every 15 seconds, can provide data analysis with the cloud service tool, and it can wirelessly program remote DAQ nodes. The goal of the mobile app is educating farmers, academia, and community members, of farming sustainably today, and for the future. The app is used as a tool to aid people in farming sustainably, teaching agricultural stewardship, and teaching environmental conservation. The app is evaluated with adaptation of 85.1%, frequency of use at 0.12 respondents/minute, and 22 respondents said they find the SmartFarm DSS as beneficial. By developing, evaluating, and demonstrating the gateway and mobile app, the SmartFarm decision support system is a viable option for improving agricultural stewardship and retaining farmers’ ownership of their data.