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Traceability, namely the ability to access information about a product and its movement across all stages of the supply chain, has been emerged as a key criterion of a product’s quality and safety. Managing fresh products, such as fruits and vegetables, is a particularly complicated task, since they are perishable with short shelf lives and are vulnerable to environmental conditions. This makes traceability of fresh produce very significant. The present study provides a brief overview of the relative literature on fresh produce traceability systems. It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards. Therefore, a user-friendly open access traceability system is proposed for the development of an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing. Various Internet of Things technologies are incorporated and connected to the web, while an android-based platform enables the monitoring of the quality of fruits and vegetables throughout the whole agri-food supply chain, starting from the field level to the consumer and back to the field. The applicability of the system, named AgroTRACE, is further extended to waste management, which constitutes an important aspect of a circular economy.
Aristotelis C. Tagarakis; Lefteris Benos; Dimitrios Kateris; Nikolaos Tsotsolas; Dionysis Bochtis. Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System. Applied Sciences 2021, 11, 7596 .
AMA StyleAristotelis C. Tagarakis, Lefteris Benos, Dimitrios Kateris, Nikolaos Tsotsolas, Dionysis Bochtis. Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System. Applied Sciences. 2021; 11 (16):7596.
Chicago/Turabian StyleAristotelis C. Tagarakis; Lefteris Benos; Dimitrios Kateris; Nikolaos Tsotsolas; Dionysis Bochtis. 2021. "Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System." Applied Sciences 11, no. 16: 7596.
The challenges of the global food supply and environment conservation require ongoing scientific observations of soil-to-plant and plant-to-environment interactions with the aim of improving agriculture resource management. This study included observations of winter wheat yield and biomass of four varieties over three consecutive growing seasons and four site-year cases to assess the effects of nitrogen (N) fertilization rate and time of application on grain yield and biomass. For different wheat varieties, the full factorial design was performed, where factorial combinations of year, location, fall and spring N applications were laid out in a randomized complete block design. The N rate significantly influenced grain yield and biomass production efficiency. The time of N application had a highly significant effect on grain yield, biomass and NUE traits. The N rate of 120 kg ha−1 was recognized as a breakpoint over which the grain yield and biomass showed a downtrend. N application in the fall had a significantly higher impact on grain yield and biomass compared to spring N application. The major contribution of wheat variability production belongs to seasonal climate circumstances (<85%) and consequential intrinsic soil properties. The average difference of grain yield between varieties was 15.75%, and 12% of biomass, respectively.
Marko Kostić; Aristotelis Tagarakis; Nataša Ljubičić; Dragana Blagojević; Mirjana Radulović; Bojana Ivošević; Dušan Rakić. The Effect of N Fertilizer Application Timing on Wheat Yield on Chernozem Soil. Agronomy 2021, 11, 1413 .
AMA StyleMarko Kostić, Aristotelis Tagarakis, Nataša Ljubičić, Dragana Blagojević, Mirjana Radulović, Bojana Ivošević, Dušan Rakić. The Effect of N Fertilizer Application Timing on Wheat Yield on Chernozem Soil. Agronomy. 2021; 11 (7):1413.
Chicago/Turabian StyleMarko Kostić; Aristotelis Tagarakis; Nataša Ljubičić; Dragana Blagojević; Mirjana Radulović; Bojana Ivošević; Dušan Rakić. 2021. "The Effect of N Fertilizer Application Timing on Wheat Yield on Chernozem Soil." Agronomy 11, no. 7: 1413.
Wireless sensor networks (WSNs) can be reliable tools in agricultural management. In this work, a low cost, low power consumption, and simple wireless sensing system dedicated for agricultural environments is presented. The system is applicable to small to medium sized fields, located anywhere with cellular network coverage, even in isolated rural areas. The novelty of the developed system lies in the fact that it uses a dummy device as Coordinator which through simple but advanced programming can receive, process, and send data packets from all End-nodes to the cloud via a 4G cellular network. Furthermore, it is energy independent, using solar energy harvesting panels, making it feasible to operate in remote, isolated fields. A star topology was followed for the sake of simplification, low energy demands and increased network reliability. The developed system was tested and evaluated in laboratory and real field environment with satisfactory operation in terms of independence, and operational reliability concerning packet losses, communication range (>250 m covering fields up to 36 ha), energy autonomy, and uninterrupted operation. The network can support up to seven nodes in a 30 min data acquisition cycle. These results confirmed the potential of this system to serve as a viable option for monitoring environmental, soil, and crop parameters.
Aristotelis Tagarakis; Dimitrios Kateris; Remigio Berruto; Dionysis Bochtis. Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards. Applied Sciences 2021, 11, 5858 .
AMA StyleAristotelis Tagarakis, Dimitrios Kateris, Remigio Berruto, Dionysis Bochtis. Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards. Applied Sciences. 2021; 11 (13):5858.
Chicago/Turabian StyleAristotelis Tagarakis; Dimitrios Kateris; Remigio Berruto; Dionysis Bochtis. 2021. "Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards." Applied Sciences 11, no. 13: 5858.
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.
Athanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors 2021, 21, 3813 .
AMA StyleAthanasios Anagnostis, Aristotelis Tagarakis, Dimitrios Kateris, Vasileios Moysiadis, Claus Sørensen, Simon Pearson, Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors. 2021; 21 (11):3813.
Chicago/Turabian StyleAthanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. 2021. "Orchard Mapping with Deep Learning Semantic Segmentation." Sensors 21, no. 11: 3813.
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
Lefteris Benos; Aristotelis Tagarakis; Georgios Dolias; Remigio Berruto; Dimitrios Kateris; Dionysis Bochtis. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758 .
AMA StyleLefteris Benos, Aristotelis Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors. 2021; 21 (11):3758.
Chicago/Turabian StyleLefteris Benos; Aristotelis Tagarakis; Georgios Dolias; Remigio Berruto; Dimitrios Kateris; Dionysis Bochtis. 2021. "Machine Learning in Agriculture: A Comprehensive Updated Review." Sensors 21, no. 11: 3758.
The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.
Athanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences 2021, 11, 2188 .
AMA StyleAthanasios Anagnostis, Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis Tagarakis, Naoum Tsolakis, Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences. 2021; 11 (5):2188.
Chicago/Turabian StyleAthanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. 2021. "Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture." Applied Sciences 11, no. 5: 2188.
This paper presents a novel approach for the detection of disease-infected leaves on trees with the use of deep learning. Focus of this study was to build an accurate and fast object detection system that can identify anthracnose-infected leaves on walnut trees, in order to be used in real agricultural environments. Similar studies in the literature address the disease identification issue; however, so far, the detection was performed on single leaves which had been removed from trees, using images taken in controlled environment with clear background. A gap has been identified in the detection of infected leaves on tree-level in real-field conditions, an issue which is tackled in our study. Deep learning is an area of machine learning that can be proved particularly useful in the development of such systems. The latest developments in deep learning and object detection, points us towards utilizing and adapting the state-of-the-art single shot detector (SSD) algorithm. An object detector was trained to recognize anthracnose-infected walnut leaves and the trained model was applied to detect diseased trees in a 4 ha commercial walnut orchard. The orchard was initially inspected by domain experts identifying the infected trees to be used as ground truth information. Out of the 379 trees of the orchard, 100 were randomly selected to train the object detector and the remaining 279 trees were used to examine the effectiveness and robustness of the detector comparing the experts’ classification with the predicted classes of the system. The best inputs and hyper-parameter configuration for the trained model provided an average precision of 63% for the object detector, which correctly classified 87% of the validation tree dataset. These encouraging results indicate that the detector shows great potential for direct application in commercial orchards, to detect infected leaves on tree level in real field conditions, and categorize the trees into infected or healthy in real time. Thus, this system can consist an applicable solution for real-time scouting, monitoring, and decision making.
A. Anagnostis; A.C. Tagarakis; G. Asiminari; E. Papageorgiou; D. Kateris; D. Moshou; D. Bochtis. A deep learning approach for anthracnose infected trees classification in walnut orchards. Computers and Electronics in Agriculture 2021, 182, 105998 .
AMA StyleA. Anagnostis, A.C. Tagarakis, G. Asiminari, E. Papageorgiou, D. Kateris, D. Moshou, D. Bochtis. A deep learning approach for anthracnose infected trees classification in walnut orchards. Computers and Electronics in Agriculture. 2021; 182 ():105998.
Chicago/Turabian StyleA. Anagnostis; A.C. Tagarakis; G. Asiminari; E. Papageorgiou; D. Kateris; D. Moshou; D. Bochtis. 2021. "A deep learning approach for anthracnose infected trees classification in walnut orchards." Computers and Electronics in Agriculture 182, no. : 105998.