ROCHA JÚNIOR, Odílio Coimbra. Desenvolvimento de um aplicativo para o manejo de irrigação utilizando a ferramenta “App inventor” na plataforma “Android”. 2015. 67 f. Dissertação (Mestrado em engenharia agrícola)- Universidade Federal do Ceará, Fortaleza-CE, 2015. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-06-28T19:44:32Z No. of bitstreams: 1 2015_dis_ocrochajunior.pdf: 3121689 bytes, checksum: e4efd8e1e745a4bd1938851b481c80e4 (MD5) / Approved for entry into archive by José Jairo Viana de Sousa (jairo@ufc.br) on 2016-06-30T22:47:34Z (GMT) No. of bitstreams: 1 2015_dis_ocrochajunior.pdf: 3121689 bytes, checksum: e4efd8e1e745a4bd1938851b481c80e4 (MD5) / Made available in DSpace on 2016-06-30T22:47:34Z (GMT). No. of bitstreams: 1 2015_dis_ocrochajunior.pdf: 3121689 bytes, checksum: e4efd8e1e745a4bd1938851b481c80e4 (MD5) Previous issue date: 2015 / The lack of a proper irrigation management by the majority of producers in irrigated public areas in the state of Ceará is considered the main factor with direct influence on productivity, cost of production, quality of products, and especially in the environmental issue, where the inappropriate use of water from our springs could seriously compromise our future generations. The initiative in the development of tools to assist the producer to carry out a satisfactory irrigation management that is accessible and of easy operation is a goal that should always be set as a priority. This work aims to develop an application for mobile devices, able to use its own database in the realization of the daily irrigation time calculation for each crop on a certain date. Techniques and software development tools were used specificly for the Android mobile platform. The application intends to assist in conducting a management with rational use of water in irrigated areas in the state of Ceará, semi-arid northeast, mitigating the lack of technical assistance specialized in precision farming, using databases with information necessary for the calculation of the need water and subsequent irrigation time recommendation. Among the installed databases, it has historical series of "potential reference evapotranspiration" for the past ten years in representative regions where the eight main irrigation schemes of the Ceará state: Curu-Paraipaba, Pentecostes, Baixo Acaraú, Araras Norte, Jaguaribe Apodi, Morada Nova, Tabuleiros de Russas e Icó-Lima Campos are located and also databases with information on the "crop coefficient" and "coverage percentage" of major crops exploited on the perimeters. This application was developed to present a new approach to irrigation management, making use of the available mobile phones in order to allow users to perform the calculation of the daily irrigation time in any of the irrigated perimeters of the Ceará state. The use of the application in simulation for the Irrigated Perimeter Baixo Acaraú demonstrated a reduction of approximately 26% over the current use of water in irrigation, allowing its use be concluded that provides good irrigation management and could result in an economy of water required to reduce the impact of recurring long and repetitive periods of drought on water resources of the state of Ceará. / A falta de um manejo adequado da irrigação por parte da maioria dos produtores instalados nos perímetros públicos irrigados no estado do Ceará é considerado como o principal fator com interferência direta na produtividade, custo de produção, qualidade dos produtos comercializados e principalmente na questão ambiental, onde o uso inadequado da água de nossos mananciais poderá comprometer seriamente nossas gerações futuras. A iniciativa de desenvolvimento de ferramentas para auxiliar o produtor a realizar um manejo de irrigação satisfatório, que seja acessível e de fácil operação é uma meta que devemos sempre definir como prioritária. Esse trabalho visa o desenvolvimento de um aplicativo para dispositivos móveis, capaz de utilizar uma base de dados própria na realização do cálculo do tempo diário de irrigação para cada cultura em uma determinada data. Foram utilizadas técnicas e ferramentas de desenvolvimento de software, específicas para a plataforma móvel Android. O aplicativo visa auxiliar na condução de um manejo com uso racional de água nos perímetros irrigados localizados no estado de Ceará, semiárido nordestino, mitigando a falta da assistência técnica especializada em agricultura de precisão, utilizando bancos de dados com informações necessárias para o cálculo da necessidade hídrica e posterior recomendação do tempo de irrigação. Entre os bancos de dados instalados, conta-se com as series históricas da “evapotranspiração potencial de referência” dos últimos dez anos nas regiões representativas onde se encontram implantados os oito principais perímetros irrigados do estado do Ceará: Curu-Paraipaba, Pentecostes, Baixo Acaraú, Araras Norte, Jaguaribe Apodi, Morada Nova, Tabuleiros de Russas e Icó-Lima Campos, além dos bancos de dados com informações relativas ao “coeficiente de cultivo” e “Percentual de cobertura” dos principais cultivos explorados nos perímetros. O sistema desenvolvido apresenta uma nova abordagem para o manejo da irrigação, fazendo uso da mobilidade disponível em celulares para permitir que os usuários realizem o cálculo do tempo de irrigação diário em qualquer um dos perímetros irrigados do estado do Ceará. A utilização do aplicativo em simulação para o Perímetro Irrigado Baixo Acaraú, demonstrou uma redução de aproximadamente 26 % em relação a utilização atual de água na irrigação, permitindo concluir-se que o seu uso proporciona um bom manejo de irrigação e poderá resultar em uma economia de água necessária para redução dos impactos recorrentes dos longos e repetitivos períodos de estiagem sobre os recursos hídricos do estado do Ceará.

Made available in DSpace on 2014-06-11T19:32:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-09-05Bitstream added on 2014-06-13T19:21:49Z : No. of bitstreams: 1 nascimento_gf_dr_rcla.pdf: 1247848 bytes, checksum: 89c8f2950afff4ac2857fc4e72433047 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O presente trabalho teve por objetivo verificar a variabilidade espacial de atributos químicos do solo, referentes à macronutrientes utilizando métodos estatísticos e geoestatísticos, em uma área considerada homogênea, contendo campo com pastagem e área de floresta. A área escolhida foi a da Fazenda Mata Verde, situada no município de Candeias do Jamari – RO, a cerca de 30 km da capital. Na fase inicial da pesquisa foi elaborado um plano amostral, a partir de uma préamostragem georreferenciada, que resultou na determinação do tamanho da amostra para cada uma das áreas estudadas, cuja opção foi coletar 130 amostras no perfil 00,20 m distribuídas em malha quadrada para cada uma das áreas. Os dados foram analisados em duas fases: análise química e análise estatística. A análise química das amostras foi realizada para identificar os teores de alumínio (Al), cálcio (Ca), matéria orgânica (MO) e potencial de hidrogênio (pH). Para a realização da análise quantitativa, os valores resultantes da análise química foram armazenados em planilhas eletrônicas com os respectivos posicionamentos geográficos. Na análise descritiva e inferências estatísticas clássicas, foram efetuados os cálculos da média, variância, desvio padrão, coeficiente de variação, coeficiente de assimetria e coeficiente de curtose, dos dados amostrados. Foram ajustados modelos teóricos de variogramas aos dados experimentais e realizado o teste de validação cruzada, bem como a construção de mapas de atributos de solo, fazendo uso de krigagem ordinária. A partir dos resultados obtidos, foi possível concluir que a ocorrência de disparidades nos mapas dos atributos Al, Ca, MO e pH, nas áreas estudadas, é um indicativo da necessidade de adoção de metodologias diferenciadas para uso da área de pastagem, considerando o manejo adotado pelo pecuarista. / The purpose of this work is to verify spatial variability of soil’s macro nutrients chemical attributes using statistical and geostatistical tools, in an area considered homogeneous containing pasture and forest. The chosen area was the Mata Verde Farm situated in Candeias do Jamari City – Rondonia State, distant 30 km from the State capital. In the initial stage a sampling plan was elaborated based upon a georeferencing presampling. This resulted in sample sizes for each one of studied areas. The chosen option was the collect of 130 samples in soil profile 00.20 m grid distributed for each one of the areas. The data was analyzed in two phases: chemical analysis and statistical analysis. The samples’ chemicals analysis were done to identify macronutrients contents Al, Ca, organic matter (MO) and pH and the values of the chemical analysis were recorded in spreadsheets with respective geographic positions. To descriptive analysis and classic statistical inferences were carried out calculating means, variance, standard deviation, variation coefficient, asymmetry coefficient and kurtosis coefficient of sampled data. The theoretic model of variograms were adjusted to experimental data and carried out a cross validation test, as well as the maps construction of soil attributes using ordinary kriging. From results was possible to conclude that the occurrence of disparities in spatial distribution maps of aluminum, calcium, organic matter and pH in studied areas is an indicative of necessity of adoption of differentiated methodologies for use of the pasture area, considering the management adopted by the cattle farmers.

Master of Agribusiness / Department of Agricultural Economics / Vincent R. Amanor-Boadu / As the integration of technology and data rises in production agriculture, John Deere dealers in North America are in a constant quest to differentiate themselves and be more than just an equipment provider. Customers with more technologically advanced products are requiring more support from the dealerships. Each dealership has a unique opportunity to provide unprecedented levels of support and each may do it in a slightly different way. This creates a challenge for Deere & Company in providing resources and support to those dealers in their endeavors. This thesis was requested by Deere & Company (John Deere) to provide the company with information on Dealer Customer Support Centers in North America. In order to provide resources and tools for dealers to be successful, it is necessary to understand what they are currently doing with customer support centers and the barriers to implementing more. An online survey was sent out to the Integrated Solutions Manager at every John Deere Dealer organization in North America. From that survey there were a total of 127 responses. The two most common forms of customer support systems that dealers are using are having Integrated Solutions Staff members take calls directly from customers and having someone within the dealership answer the phone and manually route the call to the right person for support. Data also shows that some of the less common but more technologically advanced methods of support have been implemented more in the past 12 months. Survey analysis indicates that only a small percentage of dealers have a true centralized dedicated support center for customers. This subset of dealers is utilizing several different methods to support customers. The two indicative methods are having a 1- 800 number for customers to utilize for support and having a dedicated staff to help customers remotely. Dealers are frequently using several types of tools and resources to help support customers, the most frequently occurring ones include JDLink™ and Data Management Services. Respondents indicated using many other tools to provide value to customers including John Deere Remote Display Access, clinics and optimization sessions and many others. Barriers to implementing more complex forms of customer support are numerous, the biggest of which is the cost of implementation and lack of resources to support a more sophisticated customer support system. With this information, John Deere is better positioned to provide resources and support to our dealer channel facing these challenges.

Orientador: Paulo Milton Barbosa Landim / Banca: Célia Regina Lopes Zimback / Banca: Dilermando Perecin / Banca: Marcelo Monteiro da Rocha / Banca: Roberto Wagner Lourenço / Resumo: O presente trabalho teve por objetivo verificar a variabilidade espacial de atributos químicos do solo, referentes à macronutrientes utilizando métodos estatísticos e geoestatísticos, em uma área considerada homogênea, contendo campo com pastagem e área de floresta. A área escolhida foi a da Fazenda Mata Verde, situada no município de Candeias do Jamari - RO, a cerca de 30 km da capital. Na fase inicial da pesquisa foi elaborado um plano amostral, a partir de uma préamostragem georreferenciada, que resultou na determinação do tamanho da amostra para cada uma das áreas estudadas, cuja opção foi coletar 130 amostras no perfil 00,20 m distribuídas em malha quadrada para cada uma das áreas. Os dados foram analisados em duas fases: análise química e análise estatística. A análise química das amostras foi realizada para identificar os teores de alumínio (Al), cálcio (Ca), matéria orgânica (MO) e potencial de hidrogênio (pH). Para a realização da análise quantitativa, os valores resultantes da análise química foram armazenados em planilhas eletrônicas com os respectivos posicionamentos geográficos. Na análise descritiva e inferências estatísticas clássicas, foram efetuados os cálculos da média, variância, desvio padrão, coeficiente de variação, coeficiente de assimetria e coeficiente de curtose, dos dados amostrados. Foram ajustados modelos teóricos de variogramas aos dados experimentais e realizado o teste de validação cruzada, bem como a construção de mapas de atributos de solo, fazendo uso de krigagem ordinária. A partir dos resultados obtidos, foi possível concluir que a ocorrência de disparidades nos mapas dos atributos Al, Ca, MO e pH, nas áreas estudadas, é um indicativo da necessidade de adoção de metodologias diferenciadas para uso da área de pastagem, considerando o manejo adotado pelo pecuarista. / Abstract: The purpose of this work is to verify spatial variability of soil's macro nutrients chemical attributes using statistical and geostatistical tools, in an area considered homogeneous containing pasture and forest. The chosen area was the Mata Verde Farm situated in Candeias do Jamari City - Rondonia State, distant 30 km from the State capital. In the initial stage a sampling plan was elaborated based upon a georeferencing presampling. This resulted in sample sizes for each one of studied areas. The chosen option was the collect of 130 samples in soil profile 00.20 m grid distributed for each one of the areas. The data was analyzed in two phases: chemical analysis and statistical analysis. The samples' chemicals analysis were done to identify macronutrients contents Al, Ca, organic matter (MO) and pH and the values of the chemical analysis were recorded in spreadsheets with respective geographic positions. To descriptive analysis and classic statistical inferences were carried out calculating means, variance, standard deviation, variation coefficient, asymmetry coefficient and kurtosis coefficient of sampled data. The theoretic model of variograms were adjusted to experimental data and carried out a cross validation test, as well as the maps construction of soil attributes using ordinary kriging. From results was possible to conclude that the occurrence of disparities in spatial distribution maps of aluminum, calcium, organic matter and pH in studied areas is an indicative of necessity of adoption of differentiated methodologies for use of the pasture area, considering the management adopted by the cattle farmers. / Doutor

Digital image processing is one of the potential technologies used in precision agriculture to gather information, such as seed emergence, plant health, and phenology from the digital images. Despite its potential, the rate of adoption is slow due to limited accessibility, unsuitability to specific issues, unaffordability, and high technical knowledge requirement from the clientele. Therefore, the development of open source image processing applications that are task-specific, easy-to-use, requiring fewer inputs, and rich with features will be beneficial to the users/farmers for adoption. The Fiji software, an open source free image processing ImageJ platform, was used in this application development study. A collection of four different agricultural field applications were selected to address the existing issues and develop image processing tools by applying novel approaches and simple mathematical principles. First, an automated application, using a digital image and “pixel-march” method, performed multiple radial measurements of sunflower floral components. At least 32 measurements for ray florets and eight for the disc were required statistically for accurate dimensions. Second, the color calibration of digital images addressed the light intensity variations of images using standard calibration chart and derived color calibration matrix from selected color patches. Calibration using just three-color patches: red, green, and blue was sufficient to obtain images of uniform intensity. Third, plant stand count and their spatial distribution from UAS images were determined with an accuracy of ≈96 %, through pixel-profile identification method and plant cluster segmentation. Fourth, the soybean phenological stages from the PhenoCam time-lapse imagery were analyzed and they matched with the manual visual observation. The green leaf index produced the minimum variations from its smoothed curve. The time of image capture and PhenoCam distances had significant effects on the vegetation indices analyzed. A simplified approach using kymograph was developed, which was quick and efficient for phenological observations. Based on the study, these tools can be equally applied to other scenarios, or new user-coded, user-friendly, image processing tools can be developed to address specific requirements. In conclusion, these successful results demonstrated the suitability and possibility of task-specific, open source, digital image processing tools development for agricultural field applications. / United States. Agricultural Research Service / National Institute of Food and Agriculture (U.S.)

Agriculture faces many challenges related to the increasing food demands of a growing global population and the sustainable use of resources in a changing environment. To address them, we need reliable information sources, like exploiting hyperspectral satellite, airborne, and ground-based remote sensing data to observe phenological traits through a crops growth cycle and gather information to precisely diagnose when, why, and where a crop is suffering negative impacts. By combining hyperspectral capabilities with unmanned aerial vehicles (UAVs), there is an increased capacity for providing time-critical monitoring and new insights into patterns of crop development. However, considerable effort is required to effectively utilize UAV-integrated hyperspectral systems in crop-modeling and crop-breeding tasks. Here, a UAV-based hyperspectral solution for mapping crop physiological parameters was explored within a machine learning framework. To do this, a range of complementary measurements were collected from a field-based phenotyping experiment, based on a diversity panel of wild tomato (Solanum pimpinellifolium) that were grown under fresh and saline conditions. From the UAV data, positionally accurate reflectance retrievals were produced using a computationally robust automated georectification and mosaicking methodology. The resulting multitemporal UAV data were then employed to retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework. Several approaches were evaluated to identify the best-performing regression supervised methods. An investigation of two learning strategies (i.e., sequential and retraining) and the value of using spectral bands and vegetation indices (VIs) as prediction features was also performed. Finally, the utility of UAVbased hyperspectral phenotyping was demonstrated by detecting the effects of salt-stress on the different tomato accessions by estimating the salt-induced senescence index from the retrieved Chl dynamics, facilitating the identification of salt-tolerant candidates for future investigations. This research illustrates the potential of UAV-based hyperspectral imaging for plant phenotyping and precision agriculture. In particular, a) developing systematic imaging calibration and pre-processing workflows; b) exploring machine learning-driven tools for retrieving plant phenological dynamics; c) establishing a plant stress detection approach from hyperspectral-derived metrics; and d) providing new insights into using computer vision, big-data analytics, and modeling strategies to deal effectively with the complexity of the UAV-based hyperspectral data in mapping plant physiological indicators.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Detecting crop rows from video frames in real time is a fundamental challenge in the field of precision agriculture. Deep learning based semantic segmentation method, namely U-net, although successful in many tasks related to precision agriculture, performs poorly for solving this task. The reasons include paucity of large scale labeled datasets in this domain, diversity in crops, and the diversity of appearance of the same crops at various stages of their growth. In this work, we discuss the development of a practical real-life crop row detection system in collaboration with an agricultural sprayer company. Our proposed method takes the output of semantic segmentation using U-net, and then apply a clustering based probabilistic temporal calibration which can adapt to different fields and crops without the need for retraining the network. Experimental results validate that our method can be used for both refining the results of the U-net to reduce errors and also for frame interpolation of the input video stream. Upon the availability of more labeled data, we switched our approach from a semi-supervised model to a fully supervised end-to-end crop row detection model using a Feature Pyramid Network or FPN. Central to the FPN is a pyramid pooling module that extracts features from the input image at multiple resolutions. This results in the network’s ability to use both local and global features in classifying pixels to be crop rows. After training the FPN on the labeled dataset, our method obtained a mean IoU or Jaccard Index score of over 70% as reported on the test set. We trained our method on only a subset of the corn dataset and tested its performance on multiple variations of weed pressure and crop growth stages to verify that the performance does translate over the variations and is consistent across the entire dataset.

Internet of Things (IoT) has drawn much attention in recent years. With IoT, physical world entities get connected through internet. IoT is used currently in various applications, such as environmental monitoring, control systems, farming, home automation, security and surveillance systems etc. The aim of this research is to design a low-cost, energy-efficient, reliable and scalable embedded application for the smart greenhouse monitoring system. The IoT based system designed in this thesis uses various sensors to measure the air and soil quality parameters in the greenhouse, and monitor real-time data online using web-server and mobile phone based applications. A ZigBee based wireless sensor network is implemented to transport various sensory data to the gateway. Among other contributions, the designed system develops a new routing algorithm by introducing a confirmed delivery of packets and re-routing features. We also introduced an efficient cost metric for making routing decisions within WSN using hops count, and simple bi-directional link quality estimator using PRR and current battery voltage of neighbor nodes. We also verified the stability of the system by conducting various performance tests. The system is equipped with data analytic functions for the online examination of the data. The designed system adopts event-based triggering and data aggregation methods to reduce the number of transmissions, and develops a new algorithm for such purpose. The web-server and mobile applications have user interface to display the output of the data analytic services, warning, control operations and give access to query data of the user's interest.

The Internet of Things (IoT) is an umbrella term for smart things connected to the Internet.Precision agriculture is a related concept where connected sensors can be used to facilitate, e.g. more effective farming. At the same time, Bluetooth has been making advancements into IoT with the release of Bluetooth Low Energy (BLE) or Bluetooth smart as it is also known by. This thesis describes the development of a Bluetooth Low Energy moisture- and temperature sensor intended for use in an agricultural wireless sensor network system. The sensor was evaluated based on its effectiveness in agricultural environments and conditions such as weather, elevation and in different crop fields. Bluetooth Low Energy was chosen as the technology for communication by the supervising company due to its inherent support for mobile phone accessibility.Field tests showed that the sensor nodes were largely affected by greenery positioned betweentransmitter and receiver, meaning that these would preferably be placed above growing crops foreffective communication. With ideal placement of the sensor and receiving unit, the signal wouldreach up to 100 m, meaning that a receiving unit would cover a circle area with radius 100 m.Due to Bluetooth being largely integrated in mobile devices it would mean that sensor data couldeasily be made accessible with a mobile app, rather than acquiring data from an online web server.