Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

As the Internet of Things (IoT) and the Web of Things (WoT) are becoming a reality, their interconnection with mobile phone computing is increasing. Mobile phone integrated sensors offer advanced services, which when combined with Web-enabled real-world devices located near the mobile user (e.g., body area networks, radio-frequency identification tags, energy monitors, environmental sensors, etc.), have the potential of enhancing the overall user knowledge, perception and experience, encouraging more informed choices and better decisions. This paper serves as a survey of the most significant work performed in the area of mobile phone computing combined with the IoT/WoT. A selection of over 100 papers is presented, which constitute the most significant work in the field up to date, categorizing these papers into ten different domains, according to the area of application (i.e., health, sports, gaming, transportation, and agriculture), the nature of interaction (i.e., participatory sensing, eco-feedback, actuation, and control), or the communicating actors involved (i.e., things and people). Open issues and research challenges are identified, analyzed and discussed.

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

As the Internet of Things (IoT) penetrates our everyday lives, being used to address a wide variety of real-life challenges and problems, the location of things becomes an important parameter. The exact location of measuring the physical world through IoT is highly relevant to understand local environmental conditions, or to develop powerful, personalized and context-aware location-based services and applications. This survey paper maps and analyzes the IoT based on its location dimension, categorizing IoT applications and projects according to the geospatial analytical methods performed. The survey investigates the opportunities of location-aware IoT, and examines the potential of geospatial analysis in this research area.

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

The interest in milk originating from donkeys is growing worldwide due to its claimed functional and nutritional properties, especially for sensitive population groups, such as infants with cow milk protein allergy. The current study aimed to assess the microbiological quality of donkey milk produced in a donkey farm in Cyprus using culture-based and high-throughput sequencing techniques. The culture-based microbiological analysis showed very low microbial counts, whereas important food-borne pathogens were not detected in any sample. In addition, high-throughput sequencing was applied to characterize the bacterial communities of donkey milk samples. Donkey milk mostly composed of gram-negative Proteobacteria, including Sphingomonas, Pseudomonas, Mesorhizobium, and Acinetobacter; lactic acid bacteria, including Lactobacillus and Streptococcus; the endospores forming Clostridium; and the environmental genera Flavobacterium and Ralstonia, detected in lower relative abundances. The results of the study support existing findings that donkey milk contains mostly gram-negative bacteria. Moreover, it raises questions regarding the contribution of (1) antimicrobial agents (i.e., lysozyme, peptides) in shaping the microbial communities and (2) bacterial microbiota to the functional value of donkey milk.