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"Farm management"
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From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management
The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.
Journal Article
Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC
This research describes the use of high-performance computing (HPC) and deep learning to create prediction models that could be deployed on edge AI devices equipped with camera and installed in poultry farms. The main idea is to leverage an existing IoT farming platform and use HPC offline to run deep learning to train the models for object detection and object segmentation, where the objects are chickens in images taken on farm. The models can be ported from HPC to edge AI devices to create a new type of computer vision kit to enhance the existing digital poultry farm platform. Such new sensors enable implementing functions such as counting chickens, detection of dead chickens, and even assessing their weight or detecting uneven growth. These functions combined with the monitoring of environmental parameters, could enable early disease detection and improve the decision-making process. The experiment focused on Faster R-CNN architectures and AutoML was used to identify the most suitable architecture for chicken detection and segmentation for the given dataset. For the selected architectures, further hyperparameter optimization was carried out and we achieved the accuracy of AP = 85%, AP50 = 98%, and AP75 = 96% for object detection and AP = 90%, AP50 = 98%, and AP75 = 96% for instance segmentation. These models were installed on edge AI devices and evaluated in the online mode on actual poultry farms. Initial results are promising, but further development of the dataset and improvements in prediction models is needed.
Journal Article
Two oxen ahead : pre-mechanized farming in the Mediterranean
\"This revealing study shows how careful analysis of recent farming practices, and related cultural traditions, in communities around the Mediterranean can enhance our understanding of prehistoric and Greco-Roman societies. Includes a wealth of original interview material and data from field observation Provides original approaches to understanding past farming practices and their social contexts Offers a revealing comparative perspective on Mediterranean societies' agronomy Identifies a number of previously unrecorded climate-related contrasts in farming practices, which have important socio-economic significance Explores annual tasks, such as tillage and harvest; inter-annual land management techniques, such as rotation; and intergenerational issues, including capital accumulation \"-- Provided by publisher.
Book
Two oxen ahead
TWO OXEN AHEAD This revealing study of farming practices in societies around the Mediterranean draws out the valuable contribution that knowledge of recent practices can make to our understanding of husbandry in prehistoric and Greco-Roman times. It reflects increased academic interest in the formative influence of farming regimes on the societies they were designed to feed. The author's intensive research took him to farming communities around the Mediterranean, where he recorded observational and interview data on differing farming strategies and practices, many of which can be traced back to classical antiquity or earlier. The book documents these variables, through the annual chaîne opératoire (from ploughing and sowing to harvesting and threshing), interannual schemes of crop rotation and husbandry, and the generational cycle of household development. It traces the interdependence of these successive stages and explores how cultural tradition, ecological conditions, and access to resources shape variability in husbandry practice. Each chapter identifies ways in which heuristic use of data on recent farming can shed light on ancient practices and societies.
eBook
Architecture design approach for IoT-based farm management information systems
Smart farming adopts advanced technology and the corresponding principles to increase the amount of production and economic returns, often also with the goal to reduce the impact on the environment. One of the key elements of smart farming is the farm management information systems (FMISs) that supports the automation of data acquisition and processing, monitoring, planning, decision making, documenting, and managing the farm operations. An increased number of FMISs now adopt internet of things (IoT) technology to further optimize the targeted business goals. Obviously IoT systems in agriculture typically have different functional and quality requirements such as choice of communication protocols, the data processing capacity, the security level, safety level, and time performance. For developing an IoT-based FMIS, it is important to design the proper architecture that meets the corresponding requirements. To guide the architect in designing the IoT based farm management information system that meets the business objectives a systematic approach is provided. To this end a design-driven research approach is adopted in which feature-driven domain analysis is used to model the various smart farming requirements. Further, based on a FMIS and IoT reference architectures the steps and the modeling approaches for designing IoT-based FMIS architectures are described. The approach is illustrated using two case studies on smart farming in Turkey, one for smart wheat production in Konya, and the other for smart green houses in Antalya.
Journal Article
Combating hunger and achieving food security
\"Discusses the major causes of chronic and hidden hunger and emphasizes on the need to redesign farming system to increase food production\"-- Provided by publisher.
Book
Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning
Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.
Journal Article