The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Opportunities for Dutch Biorefineries

Deze Roadmap Bioraffinage beschrijft een aantal mogelijke routes naar de ontwikkeling en implementatie van een bioraffinage-gerelateerde Bio-based Economy in Nederland. De Roadmap combineert korte- en middellange termijn mogelijkheden (commerciële implementatie, demonstratie plants, pilot plants en gerelateerd toegepast onderzoek) met strategisch onderzoek voor de langere termijn. Tevens zijn vier z.g. Moonshots uitgewerkt, als voorziene bioraffinagestrategieën met een grote potentie voor de Nederlandse economi

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Analysis Into Artificial Intelligence And Its Developing Dynamic And Relationship In Agricultural Supply Chains

The thesis explores artificial intelligence (AI) in agricultural (Ag) supply chains (SCs) and presents a new typology to understand artificial intelligence-based solutions in agricultural SCs. The thesis was performed utilizing a research-based review to investigate the current uses of artificial intelligence-based solutions in agricultural SCs. The AI-based solutions were found in case studies that reviewed AI operations in different areas internationally. The typology was formed on the foundation of two dynamics, the location of AI applications in Ag SCs and the driving values to integrate the AI applications. In order to develop the typology, the AI applications were studied in a series of different analyses. The analyses helped to critique and scrutinize the AI applications to gain new perspectives. The series of analyses consists of exploring the AI applications’ location within the supply chain, the value additions to the supply chain from integrating the AI applications, and the resulting depth of the effect of AI application has on the supply chain. Each additional evaluation of the AI applications examining another parameter further exposed more insight and started to build a structured ideology of AI. The proposed typology aims to create a tool of measurement to infer AI technology’s relation in the SCs and create a new viewpoint that will lead investigation and provide insight for predictions of AI’s future in agricultural SCs. In addition, the new typology should aid agriculture firms in understanding and capturing the potential synergies stemming from the driving values of innovation. The study found that AI applications with a strong relationship in the supply chain provide the greatest beneficiary relationship between technology value creation and supply chain logistics. Furthermore, AI applications will have the strongest relationship and implementation when operating in collaboration with other supply chain locations and AI integrated firms. Concluding the thesis, relevant policy and business practice recommendations are proposed

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Crop and animal production techniques have changed significantly over the last century. In the early 1900s, animal power was replaced by tractor power that resulted in tremendous improvements in field productivity, which subsequently laid foundation for mechanized agriculture. While precision agriculture has enabled site-specific management of crop inputs for improved yields and quality, precision livestock farming has boosted efficiencies in animal and dairy industries. By 2020, highly automated systems are employed in crop and animal agriculture to increase input efficiency and agricultural output with reduced adverse impact on the environment. Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. This Issue Paper presents opportunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adoption. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training needs of the workforce who will be involved in the next-generation agriculture

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images