Modelagem e persistência de dados sensoriais para a pecuária de precisão: Sensor data for precision livestock: modeling and persistence

Para manter-se competitivo na cadeia de produção bovina, é importante inovar na produção e gestão do agronegócio. Desse modo, surgem alguns desafios, como o aumento da demanda relacionada ao bem-estar animal, a segurança alimentar, a rastreabilidade bovina, a sustentabilidade, entre outros. A pecuária de precisão pode contribuir para atingir esses objetivos. Diversos estudos para a pecuária de precisão são desenvolvidos em parceria entre a Embrapa Gado de Corte e a Faculdade de Computação (FACOM), da Universidade Federal do Mato Grosso do Sul (UFMS). Esses são projetos que possuem uma heterogeneidade de sensores e geram um grande volume de dados provenientes de diferentes fontes e padrões. Este trabalho tem como finalidade definir um modelo de dados semântico, bem como um modelo físico para persistência dos dados oriundos dos diversos sensores e sistemas utilizados na pecuária de precisão, visando à integração e ao compartilhamento padronizado dessas informações

Precision Agriculture for Water Management Using IOT

In the territory of agriculture, proper use of irrigation is important and it is well known that irrigation by drip approach is very cost effective and efficient.Role of agriculture in the development of agricultural country is very important. The freshly come up wireless sensor network (WSN) technology has growing rapidly into distinct multi-disciplinary fields. Agriculture and farming is one of the management which have freshly switch their consideration to WSN, curious this cost adequate technology to improve its production and boost agriculture yield definitive. The outlook of this paper is to design and develop an agricultural monitoring system using wireless sensor network and IOT to enlarge the productivity and quality of farming without penetrating it for all the time manually. Temperature, humidity and water levels are the most important circumstances for the productivity, growth, and quality of plants in agriculture. The temperature, humidity and water level sensors are set up to cluster the temperature and humidity values. One of the most stimulating fields having an exotic need of decision support systems is Precision Agriculture (PA). Through sensor networks, agriculture can be associated to the IoT, with the help of this approach which provides real-time information about the lands and crops that will help farmers make right decisions. The primary influence is implementation of WSN in Precision Agriculture (PA) with the help of IoT which will enhance the usage of water, fertilizers while expand the yield of the crops and also notifications are sent to farmers mobile periodically. The farmers can able to monitor the field conditions from anywhere

Semantic Segmentation based deep learning approaches for weed detection

Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of ‘Focal loss’ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications Advisor: Yeyin Sh