Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

Uganda’s banana industry is heavily impeded by the lack of cheap, reliable and sustainable energy mainly needed for processing of banana fruit into pulp and subsequent drying into chips before milling into banana flour that has several uses in the bakery industry, among others. Uganda has one of the lowest electricity access levels, estimated at only 2–3% in rural areas where most of the banana growing is located. In addition, most banana farmers have limited financial capacity to access modern solar energy technologies that can generate sufficient energy for industrial processing. Besides energy scarcity and unreliability, banana production, marketing and industrial processing generate large quantities of organic wastes that are disposed of majorly by unregulated dumping in places such as swamps, thereby forming huge putrefying biomass that emit green house gases (methane and carbon dioxide). On the other hand, the energy content of banana waste, if harnessed through appropriate waste-to-energy technologies, would not only solve the energy requirement for processing of banana pulp, but would also offer an additional benefit of avoiding fossil fuels through the use of renewable energy. The potential waste-to-energy technologies that can be used in valorisation of banana waste can be grouped into three: Thermal (Direct combustion and Incineration), Thermo-chemical (Torrefaction, Plasma treatment, Gasification and Pyrolysis) and Biochemical (Composting, Ethanol fermentation and Anaerobic Digestion). However, due to high moisture content of banana waste, direct application of either thermal or thermo-chemical waste-to-energy technologies is challenging. Although, supercritical water gasification does not require drying of feedstock beforehand and can be a promising thermo-chemical technology for gasification of wet biomass such as banana waste, it is an expensive technology that may not be adopted by banana farmers in Uganda. Biochemical conversion technologies are reported to be more eco-friendly and appropriate for waste biomass with high moisture content such as banana waste. Uganda’s banana industrialisation is rural based with limited technical knowledge and economic capability to setup modern solar technologies and thermo-conversions for drying banana fruit pulp. This review explored the advantages of various waste-to-energy technologies as well as their shortfalls. Anaerobic digestion stands out as the most feasible and appropriate waste-to-energy technology for solving the energy scarcity and waste burden in banana industry. Finally, potential options for the enhancement of anaerobic digestion of banana waste were also elucidated.

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.

Group pig lying change detection using Delaunay triangulation is presented.Ellipse fitting algorithms were used to localize each pig body in the image.Delaunay triangulations were changed as the room temperature increased.Pig lying location is determined through ellipse centroids in the pen. Pig lying patterns can provide information on environmental factors affecting production efficiency, health and welfare. The aim of this study was to investigate the feasibility of using image processing and the Delaunay triangulation method to detect change in group lying behaviour of pigs under commercial farm conditions and relate this to changing environmental temperature. Two pens of 22 growing pigs were monitored during 15days using top view CCD cameras. Animals were extracted from their background using image processing algorithms, and the x-y coordinates of each binary image were used for ellipse fitting algorithms to localize each pig. By means of the region properties and perimeter of each Delaunay Triangulation, it was possible with high accuracy to automatically find the changes in lying posture and location within the pen of grouped pigs caused by temperature changes.