Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

Design and Construction of an Automation Tool for Feeding Pokdakan Pesawaran Fish

This study focuses on developing an automated feeding tool for fish farming in Pesawaran, a region known for its aquaculture potential. Aquaculture, crucial for global food needs, requires efficient and sustainable management, especially in feeding, a critical factor in fish growth and health. Traditional manual feeding methods are time-consuming and prone to errors, affecting fish productivity and growth. The research aimed to enhance feed management efficiency, minimize feeding errors, and improve the sustainability and productivity of fish farming in Pesawaran. The initial phase involved analyzing the needs of fish farmers, environmental factors, and fish species. The design of the automation tool emphasized ergonomics, reliability, and ease of use. The Rapid Application Development (RAD) method was employed, focusing on quick and iterative development. This method was applied at the Pokdakan Pemuda Tani RPL in Negeri Sakti Village, Gedong Tataan District, Pesawaran Regency, from July to November 2023. The application of RAD in designing the Pokdakan Pesawaran fish-feeding automation tool yielded positive outcomes. The fast, responsive development process, which actively involved users, led to a practical solution well-received by the fish farming community. This research demonstrates the value of RAD principles in providing practical, locally relevant solutions and guiding the development of adaptive, user-oriented aquaculture technology

Real-time vital signs monitoring system for livestock

The focus on the application of information and communication technologies & electronics (ICTE) in agriculture has proved to be very efficient and revolutionary in several ways. With the adoption of increasingly efficient and modern technologies, agriculture, in general, improves its competitiveness and production is carried out in a more sustainable way. The intensive use of ICTE in this sector has been aimed at creating integrated solutions that generate efficiency gains in productivity, sustainability and economic, social and environmental quality. This type of technologies, when aimed at monitoring livestock, have some characteristics in common. Precise positioning and geolocation from GPS, geographic mapping, sensors and communication systems are some of the tools that will allow the development of a complete and extremely accurate system for monitoring vital signs. This proposal for a cattle or equine ICTE-based monitoring system is developed as a belt. It contains a microcontroller that is used to evaluate the animal's heart rate and detect abnormal mobility. The correct evaluation of these two parameters proves very useful for the detection of many of the pathologies and anomalies that constitute economic losses for the producers. With accurate monitoring, it is possible to circumvent these events that are detrimental to animal production.O foco na aplicação de tecnologias de informação e comunicação e eletrónica (TICE) na agricultura provou ser muito eficiente e revolucionário de várias maneiras. Com a adoção de tecnologias cada vez mais eficientes e modernas, a agricultura em geral melhora sua competitividade e a produção é realizada de forma mais sustentável. O uso intensivo de TICE neste setor tem por objetivo criar soluções integradas que gerem ganhos efetivos em produtividade, sustentabilidade e qualidade económica, social e ambiental. Este tipo de tecnologias, quando destinadas à monitoração de gado, têm algumas características em comum. Posicionamento preciso e geolocalização de GPS, mapeamento geográfico, sensores e sistemas de comunicação são algumas das ferramentas que permitirão o desenvolvimento de um sistema completo e preciso para monitorar sinais vitais de animais. Esta proposta para um sistema de monitorização de gado ou equinos, baseado em TICE, é desenvolvido como um cinto. Este contém um dispositivo microcontrolador que é usado para avaliar a frequência cardíaca do animal e detetar mobilidade anormal. A avaliação correta desses dois parâmetros mostra-se muito útil para a deteção de muitas das patologias e anomalias que constituem perdas económicas para os produtores. Com uma monitorização precisa, é possível contornar estes eventos prejudiciais a uma produção animal

8th Annual Research in the Capitol [Program], March 26, 2013

Program of research presentations given at the Capitol by students from the University of Northern Iowa, Iowa State University, and the University of Iowa.https://scholarworks.uni.edu/programs_rcapitol/1009/thumbnail.jp

Mechatronics applications and prototyping sensors for the precision livestock farming

The study is subdivided into 5 chapters and comprises a review of the main components of Plf, the development of a prototype for EC monitoring in ewe milk, a prototype for monitoring animals body temperature, the optimization of collection rounds of goat milk and the development of a prototype for somatic cell count (SCC) through the measurement of Sodium ions in ewe milk.‬‬‬ The first chapter is a review of the advancements of the main components of Plf, i.e. software, hardware and data transmission, focusing on issues related to hardware modularity and differences between licensed and unlicensed software. From the review it emerges that image processing is one of the most used techniques in Plf systems, in that it allows the detection of behavioral, biological and pathological parameters without interfering with the animals routine activities. In this regard the area occupied by a lamb carcass was calculated by using an image analysis open source software, CellProfiler (Jones et al., 2008). The second chapter deals with the realization of an innovative portable tool for somatic cells count in ewe milk by measuring its electrical conductivity. There are over 15,000 dairy sheep farms in Sardinia, which represent both historically and economically the most important agricultural and livestock sector in the island. Indeed, Sardinia holds more than 40% of the national sheep population thanks to more than 3 million sheep heads that provide about 60% of the total national milk production. One of the most common problems in sheep farms is mastitis, an intramammary infection which may cause a quantitative reduction up to 50% in milk production and a qualitative drop, in particular of lactose and casein. One of the indirect methods for the assessment of somatic cell count (SCC) in ruminants’ milk is through the measurement of its electrical conductivity (EC). In small ruminants, EC has a reasonable correlation R2 = 0.35 with somatic cells but to date there is still not a portable tool that can estimate SCC based on the milk’s EC reading. The prototype was calibrated on Sarda ewe milk. The aim of Chapter 3 was to develop a system using a open source sensors, actuators and micro-controller. The system is able to monitoring the rectal temperature of the animals, sending data via Bluetooth to a smart phone. The micro-controller used was an ATmega32U4, the temperature was read using the LM35 analogic sensor and a Class 1 Bluetooth serial module was connected to Arduino creating a wireless serial link between an Android phone and the Arduino board. The application for receiving data on an android smart phone was created using App Inventor that is an innovative Android application creation software developed by Massachusetts Institute of Technology (MIT). This app is free available on Google Play Store under the name animal_temp. The costs of sheep milk collection rounds in Sardinia have been analysed in chapter fourth. The escalating costs incurred by the dairy processing industries for milk collection from individual farms have focused the attention on the rationalization of milk collection and transport systems. In this regard, the case of the Sardinian goat sector has characteristics that make it unique and not comparable to other logistics optimization realities. The problems of this sector are mainly represented by the particular conditions of the rural road network and the fragmented nature of livestock farms. The aim of the present study was to test a milk collection route optimization software, MilkTour, in the collection rounds of a sample cheese dairy. The software has been developed by the Land Engineering Section of the Agriculture Department of the University of Sassari. A total of 5 routes were analysed and optimized. The results have highlighted the importance of optimizing collection routes as they have a significant impact on business costs. A important contribution that has emerged is the strong correlation between collection density and the cost per litre of collected milk (€cent/l), which allows to detects the cost-effectiveness of a round of collection and its relative optimized around. The objective of chapter 5 was to study the relationship between the ione Na+ and the main components of sheep milk, in particular somatic cells. Moreover, a portable device for estimating SCC in sheep milk was designed. The study was conducted on over 2000 samples. The milk components examined were: fat, proteins, lactose, pH, sodium chloride, urea and the ions Na+. The correlation between Na + and SCC corresponded to R2 = 0.76 (P <0.01). The prototype developed incorporates two containers which receives milk samples taken from each half udder. Each container has integrated inside two sensors, one to detect the level of Na+ in the milk and the other one to compensate the milk temperature. The mathematical model, loaded into the microcontroller by a firmware written in C / C ++, analyze the data and gives back the estimate of SCC level, so it allows farmers to monitor the ewes health status by periodically comparing the somatic cell counts of each half udder. While dealing with different topics the 5 chapters can be enclose in a big new topic, called Precision Livestock Farming (Plf). Plf is the discipline that allows to monitor in real-time the numerous biological and environmental parameters concerning each individual animal of the herd. A Plf system is always made up by three components: a physical element, i.e. the hardware; an element for data processing and presentation, known as the software; and an element for the transmission of data, i.e. the network. The hardware comprises the sensors, the computers and/or microcontrollers, the data transmission and acquisition systems and the actuators. Mathematical models for data processing and the data presentation interface are included in the software loaded into the microcontroller

Launching the Grand Challenges for Ocean Conservation

The ten most pressing Grand Challenges in Oceans Conservation were identified at the Oceans Big Think and described in a detailed working document:A Blue Revolution for Oceans: Reengineering Aquaculture for SustainabilityEnding and Recovering from Marine DebrisTransparency and Traceability from Sea to Shore:  Ending OverfishingProtecting Critical Ocean Habitats: New Tools for Marine ProtectionEngineering Ecological Resilience in Near Shore and Coastal AreasReducing the Ecological Footprint of Fishing through Smarter GearArresting the Alien Invasion: Combating Invasive SpeciesCombatting the Effects of Ocean AcidificationEnding Marine Wildlife TraffickingReviving Dead Zones: Combating Ocean Deoxygenation and Nutrient Runof

Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep

Lameness can be described as painful erratic movements, which relate to a locomotor system and result in the animal deviating from its normal gait or posture. Lameness is considered one of the major health and welfare concerns for the sheep industry in the UK that leads to a substantial economic problem and causes a reduction in overall farm productivity. According to a report in 2013 by ADAS entitled ‘Economic Impact of Health and Welfare Issues in Beef, Cattle and Sheep in England’, each lame ewe costs £89.80 due to the decline in body condition, lambing percentage, growth rate, and reduced fertility. Thus, early lameness detection eliminates the negative impact of lameness and increase the chance of favourable outcome from treatment. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal behaviours or movements which relate to lameness. The aim of this thesis was to evaluate the feasibility and accessibility of a proposed data mining approach (SLDM) to detect the early signs of lameness in sheep via analysing the retrieved data from a mounted wearable motion sensor within a sheep’s neck collar through investigating the most cost effective factors that contribute to lameness detection within the whole data mining process including; sensor sampling rate, segmentation methods, window size, extracted features, feature selection methods, and applicable classification algorithm. Three classes are recognised for sheep while their walking throughout the data collection process (sound, mild, and severe lameness classes). The sheep data were collected using three different sensor applications (Sheep Tracker, SensoDuino, SensorLog) which collect sheep data movements at different sampling rates 10, 5, and 4 Hz. Various sensing data were retrieved in X,Y, and Z dimensions; however, only accelerometer, gyroscope, and orientation readings are considered in the current study. Four sheep datasets are aggregated each of which includes 31, 10, 18, and 7 sheep. The conducted work in this thesis evaluates the performance of ensemble classifiers (Bagging, Boosting, or RusBoosting) using three different validation methods (5-fold, 0.3 hold-out, and proposed one ‘Single Sheep Splitting’) in comparison to three sampling rates (10, 5, 4 Hz), two segmentation approaches (FNSW and FOSW), three feature selection methods (ReliefF, GA, and RF) and three window sizes (10, 7, 5 sec.). Promising results of lameness prediction accuracies are achieved over most of the combinations (3 sampling rates, two segmentation methods, 3 window sizes, 183 extracted features, 3 feature selection methods, 3 ensemble classification models, and 3 model validation methods). However, the highest accuracy is revealed by using the `Bagging ensemble classifier 88.92% with F-score of 87.7%, 91.1%, 88.2% for sound walking, mildly walking, and severely walking classes, respectively. The results are obtained using 5-fold cross-validation over a 10 sec.window for sheep data collected at 10 Hz sampling rate using only the accelerometer hardware sensor reading and calculated orientation readings. The number of features selected is 46 optimised by GA using CHAID tree as a fitness function. Conversely, the lowest prediction accuracy of 56.25% with F-score (63.4% sound walking, 51.9% mildly walking, 48.8% severely walking) is recorded when RusBoosting ensemble is applied using 5-fold cross-validation over a 10 sec.window for dataset collected at the 4 Hz. sampling rate. So, the major research findings recommend that 10 Hz sampling rate is adequate for collect sheep movements, while the best segmentation method is FOSW as 20% of data-points are shared between two successive windows. Whereas, the preferable number of data-points (sheep movements) to be pre-processed is around 100, which is obtained when a 10 sec.window size or 7 sec.window size is applied. Additionally, the 20 features selected by RF out of 183 features could reveal good accuracy results compared to the whole set of extracted features. Although that GA feature selection method has slower execution time than RF, competitive prediction accuracy could be achieved when the selected features by GA were fed to the classifier. Finally, the acceleration sensor data alone are capable of making the decision about the lame sheep. So no extra hardware sensors like Gyroscope is required for decision making; moreover, the orientation sensor features could be directly derived from Acc which contribute most to lameness detection. Since the most cost effective factors are identified in this research, the practice in the meanwhile could be applicable for farmers, stakeholders, and manufacturers as no available sensor to detect the lame sheep developed yet. Therefore, the multidisciplinary nature of the conducted research opens diverse paths towards applying further research studies to develop various data mining approaches and practical sensor kits to detect the early signs of sheep’s lameness for better farm productivity and sheep industry prosperity in the UK

The Response of Beef Cattle to Disturbances from Unmanned Aerial Vehicles (UAVs)

Unmanned aerial vehicles (UAVs) are increasingly becoming common in animal agriculture. However, research regarding the impact of UAV disturbance on animal wellbeing is lacking or limited. The goal of this study was to investigate the effect of UAV flights on beef cattle by measuring cattle heart and movement rate when introduced to single or multiple UAV flights. A total of 16 -18 crossbred beef heifers were introduced to different flights patterns at between 5 and 9 m above ground level (AGL) at approximately 1 to 2 m/s horizontal velocity for 4 weeks with flights repeated 3 days per week. Results from the study showed that single UAV flights conducted in (i) circular and (ii) grid pattern flights had no significant effect on heifer heart and movement rate. However, multiple (i) circular pattern and (ii) approach style flights increased heifer heart rate when first introduced to UAVs, but repeated flights resulted in habituation. Moreover, heifers first introduced to circular pattern flights were likely to flee but became habituated after repeated flights. However, heifers introduced to approach style flights showed more fleeing behavior even after repeated flights. The findings of this study will provide information for safely using UAVs in cattle health and behavior monitoring