Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

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

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

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

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

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. © 2022 by the authors

Phenotypic, Genetic, and Epigenetic Variation among Diverse Sweet Cherry Gene Pools

Sweet cherry germplasm contains a high variety of phenotypes which are associated with fruit size and shape as well as sugar content, etc. High phenotypic variation can be a result of genetic or epigenetic diversity that may interact through time. Recent studies have provided evidence that besides allelic variation, epiallelic variation can establish new heritable phenotypes. Herein we conducted a genetic and an epigenetic study (using amplified fragment length polymorphism (AFLP) and methylation-sensitive amplified polymorphism (MSAP) markers, respectively), accompanied by phenotypic traits correlation analysis in sweet cherry gene pools. The mean genetic diversity was greater than the epigenetic diversity (hgen = 0.193; hepi = 0.185), while no significant relationship was found between genetic and epigenetic distance according to a Mantel test. Furthermore, according to correlation analyses our results provided evidence that epigenetic diversity in predefined populations of sweet cherry had a stronger impact on phenotypic traits than their rich genetic diversity

Acceptability of Self-Sampling for Human Papillomavirus-Based Cervical Cancer Screening

Background: Human papillomavirus (HPV)-DNA testing combined with self-sampling could increase cervical cancer screening effectiveness, utilizing a sensitive screening modality and an easy sampling method with minimal pain or discomfort. Self-sampling acceptability, though, is pivotal. Materials and Methods: This study is a nested cross-sectional survey within GRECOSELF, a cross-sectional study on HPV-based screening with self-sampling, aiming at investigating self-sampling acceptability among Greek women residing in rural areas, and the factors affecting it. Women between 25 and 60 years old were recruited by midwives participating in a nationwide midwifery network. Participants, after self-sampling, filled out a questionnaire with three sections, one regarding demographic characteristics, a second with questions pertaining to the participants' cervical cancer screening history, and a third with questions regarding the self-sampling process per se. Results: The sample included 13,111 women. Most participants (67.9%), including those screened or not in the past, would prefer self-sampling if assured that the results are not inferior to standard testing. Discomfort or pain during self-sampling was absent or minimal in 97.1% and 96.5% of the cases, respectively, and 74.4% of the women felt adequately confident that they followed the instructions correctly. Women mostly preferred self-sampling at home compared with health care facilities. Pain and discomfort during the procedure, although rare, were significant factors against acceptance. Most of the women reporting a negative impression had a negative experience with conventional sampling in the past. Conclusion: Self-sampling is highly acceptable. Acceptance can be further improved with proper communication of the process and its noninferiority compared with conventional screening. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020