Procedures and Benefits of an Integrated Soil Mapping System for Directed Soil Sampling

There is an increasing need for the development of information systems that can optimize field management by leveraging the information obtained from the huge volume of data related to spatial variation within agricultural fields. To that end, an integrated soil mapping system for directed soil sampling is presented in this paper. The system architecture is analysed, highlighting the interactions between the individual subsystems toward capturing their internal structure. The final product constitutes a useful and effective tool for supporting field management as a result of in-depth study using state-of-the-art sensors, data fusion and decision-making algorithms. The benefits of using such a system are multifold including: (a) Optimization of the application of inputs on the farm; b) Reduction of the environmental footprint of agricultural practices; c) Increase of the economic benefit from the cultivation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

Path Planning for Autonomous Robotic Platform based on Created Sampling Maps

Soil properties are of great importance in crop management, as they highly affect plant growth, crop production and product quality. In order to examine these properties, soil samples must be collected from the entire surface of the field. An effective soil sampling requires careful selection of the total number and the location of the samples. Therefore, soil properties can present heterogeneity along the field. For that reason, distributing sampling points evenly along the field is not considered as a best practice. In this research, in order to define the location of sampling points, the field was divided into homogenous management zones based on electrical conductivity (ECa) values. An equal number of points was distributed in each zone and a sampling map was created. Subsequently, a path for autonomous navigation was generated based on the created sampling map. More specifically, points of the map were distributed in the shortest possible distance order for the robotic platform to move while collecting the samples. In order to test the accuracy of the path planning, the proposed path was uploaded to the robotic platform and the movement was mapped. The path that was followed by the robotic platform was quite similar to the simulated path. The results of this research suggest that sensors such as a penetrometer can be mounted on an autonomous robotic platform in order to collect data from sampling points by moving along the created path. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

A Real-time Approach System for Vineyards Intra-row Weed Detection

With the incorporation of autonomous robotic platforms in various areas (industry, agriculture, etc.), numerous mundane operations have become fully automated. The highly demanding working environment of Agriculture let the development of techniques and machineries that could cope with each case. New technologies (from high performance motors to optimization algorithms) have been implemented and tested in this field. Every cultivation season, there are several operations that contribute to crop development and have to take place at least once. One of these operations is the weeding. In every crop, there are plants that are not part of it. These plants, in most cases have a negative impact on the crop and had to be removed. In the past the weeding was taken place either by hand (smaller fields) or by the use of herbicides (larger fields). In the second case, the dosage and the time are pre-defined, and they are not taking into consideration the growth percentage and the weed allocation within the field. In this work, a novel approach for intra-row weed detection in vineyards is developed and presented. All the experiments both for data collection and algorithm testing took place in a high value vineyard which produce numerous wine varieties. The aim of this work is to implement an accurate real-time robotic system for weed detection and segmentation using a deep learning algorithm in order to optimize the weeding procedure. This approach consists of two essential sub-systems. The first one is the robotic platform that embeds all the necessary sensors and the required computational power for the detection algorithm. The second one is the developed algorithm. From all the developed models, the selected one performed accurately in the training procedure and in the unknown datasets. In order to properly validate the algorithm, the unknown datasets were acquired in different time periods with variations in both camera angle and wine varieties. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

A deep learning approach for anthracnose infected trees classification in walnut orchards

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. © 2021 Elsevier B.V

AgroTRACE: A Complete Fresh Fruits and Vegetables Traceability System

The fresh food industry recognizes the importance of traceability and food safety; however, some sectors are considered more advanced than others in implementing the relevant processes throughout the supply chain. At the international level, the branches of industry and the key players in the management of the supply chain work together to co-create an integrated and consolidated traceability process in order to benefit all the subcategories of fresh food products, such as seafood, dairy, baked goods, meat, poultry, fruits and vegetables. Therefore, an effective tracking process needs to be based on a standard approach to fresh produce and its location recognition, while at the same time remaining flexible in the individual roles and responsibilities of the various links in the supply chain within the ecosystem. While many trading partners already have interfaces with external systems and processes for some level of traceability of their products, the next necessary step towards an integrated approach is to identify interoperability opportunities between internal and external processes across the food industry. Towards this direction, the AgroTRACE system aims to achieve end-to-end traceability of a fresh product supply chain through the deployment system, which combines internal and external tracking processes, so that each user is able to identify the immediate source and immediate recipient of the products. The system applies the “one step up, one step down” principle to provide effective tracking in the supply chain. In particular, each distinct product is recognized globally and in a unique way so that it can be located upstream and downstream of the supply chain. The innovation of the proposed system is further enhanced by the fact that the tracking will go beyond the route from field to field and covers the part of recycling (biomass, compost, etc.), in the context of the circular economy. That is, implement traceability from the field-to the shelf-to the field. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

Orchard mapping with deep learning semantic segmentation

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field's tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts' orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery

The Cutting Edge on Advances in ICT Systems in Agriculture †

[No abstract available

An Integrated Real-Time Hand Gesture Recognition Framework for Human–Robot Interaction in Agriculture

Incorporating hand gesture recognition in human–robot interaction has the potential to provide a natural way of communication, thus contributing to a more fluid collaboration toward optimizing the efficiency of the application at hand and overcoming possible challenges. A very promising field of interest is agriculture, owing to its complex and dynamic environments. The aim of this study was twofold: (a) to develop a real-time skeleton-based recognition system for five hand gestures using a depth camera and machine learning, and (b) to enable a real-time human–robot interaction framework and test it in different scenarios. For this purpose, six machine learning classifiers were tested, while the Robot Operating System (ROS) software was utilized for “translating” the gestures into five commands to be executed by the robot. Furthermore, the developed system was successfully tested in outdoor experimental sessions that included either one or two persons. In the last case, the robot, based on the recognized gesture, could distinguish which of the two workers required help, follow the “locked” person, stop, return to a target location, or “unlock” them. For the sake of safety, the robot navigated with a preset socially accepted speed while keeping a safe distance in all interactions. © 2022 by the authors

Comparison of signal processing techniques for condition monitoring based on artificial neural networks

The paper presents the results of a study aimed to compare different signal processing techniques for the condition monitoring of a mechanical system for indexing motion. Artificial feed-forward neural networks (ANN) are used as classifiers. The mechanical system can work in different conditions (variable loads and velocities, lubricant oil with different viscosity) and the ANN identifies the working condition. The monitored variable is the acceleration signal of the rotating table, opportunely pre-processed. The signal processing techniques compared are: Power Spectral Density (PSD), Fast Fourier Transform (FFT), Wavelet, Amplitude Probability Density Function (PDF), Higher Order Spectra (HOS)