Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant’s phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

BLOB Analysis for Fruit Recognition and Detection

Robot application in agriculture can ease the farming process, especially as the harvesting robot for seasonal fruit that is available in a short time. The addition of "eye" as the image sensor is an important feature for a harvesting robot. Thanks to the increment of technology, the camera is getting smaller with better performance, and lower prices. The cheap sensors and components make the creation of cheap and effective robot possible. Image processing is necessary for object detection, and open source software is available now for this purpose. This paper proposes BLOB analysis for object detection of 5 fruits with different shapes and colors. The simulation results show that the proposed method is effective for object detection regardless the shapes, colors, and noises

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

Line-based deep learning method for tree branch detection from digital images

The final publication is available at Elsevier via https://doi.org/10.1016/j.jag.2022.102759. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensePreventive maintenance of power lines, including cutting and pruning of tree branches, is essential to avoid interruptions in the energy supply. Automatic methods can support this risky task and also reduce time consuming. Here, we propose a method in which the orientation and the grasping positions of tree branches are estimated. The proposed method firstly predicts the straight line (representing the tree branch extension) based on a convolutional neural network (CNN). Secondly, a Hough transform is applied to estimate the direction and position of the line. Finally, we estimate the grip point as the pixel point with the highest probability of belonging to the line. We generated a dataset based on internet searches and annotated 1868 images considering challenging scenarios with different tree branch shapes, capture devices, and environmental conditions. Ten-fold cross-validation was adopted, considering 90% for training and 10% for testing. We also assessed the method under corruptions (gaussian and shot) with different severity levels. The experimental analysis showed the effectiveness of the proposed method reporting F1-score of 96.78%. Our method outperformed state-of-the-art Deep Hough Transform (DHT) and Fully Convolutional Line Parsing (F-Clip).This research was funded by CNPq (p: 433783/2018–4, 310517/2020–6, 314902/2018–0, 304052/2019–1 and 303559/2019–5), FUNDECT (p: 59/300. 066/2015, 071/2015) and CAPES PrInt (p: 88881.311850/2018–01). The authors acknowledge the support of the UFMS (Federal University of Mato Grosso do Sul) and CAPES (Finance Code 001). This research was also partially supported by the Emerging Interdisciplinary Project of Central University of Finance and Economics

Artificial Intelligence for detection and prevention of mold contamination in tomato processing

openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto estetico. Essa è causata principalmente da funghi che si diffondono rapidamente sulla superficie dei pomodori. Tale processo compromette così la qualità con la conseguente produzione di tossine che possono influire sulla salute umana. L'obiettivo sperimentale di questo lavoro è il problema dello spreco e della perdita di prodotto nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua, energia e tempo. Il proposito è testare, anche nella fase iniziale, la capacità di un algoritmo di rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e creare le epoche di addestramento è stato quindi utilizzato YOLOv7, l'algoritmo di rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state utilizzate metriche di valutazione, tra cui “Precision” e “Recall”. L'ipotesi di applicazione dell'intelligenza artificiale in futuro sarà un grande potenziale per migliorare i processi di produzione alimentare, facilitando, così, l'identificazione delle muffe. Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti contaminati, riducendo così il rischio di diffusione delle tossine e preservando la qualità degli alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi alimentari e le inefficienze delle risorse associate allo scarto di grandi quantità di prodotto. Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un rilevamento accurato e tempestivo. Questa tecnologia potrà offrire, dando priorità alla prevenzione, una promettente opportunità per migliorare la qualità, l'efficienza e la sostenibilità dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect mold in tomatoes during the drying process. Mold presence in tomatoes poses threats to human health and the food industry as it leads to several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the tomato surface, compromising their quality, and potentially producing toxins that can harm human health. The experimental aim of this work focused on the issue of wastage and loss within the food industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting in a loss of food and resources. Considering that tomato production requires resources such as land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these valuable resources. The goal was to evaluate the mold detection capabilities of an object detection algorithm, particularly in its early stages, to facilitate preventative measures. This experimental analysis entailed training the algorithm with an extensive array of images, encompassing a variety of healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized for image labeling and training epochs. Evaluation metrics, including precision and recall, were utilized to assess the algorithm's performance. The implementation of artificial intelligence in the future has significant potential for enhancing food production processes by streamlining mold identification. Prompt mold detection would expedite segregation of contaminated products, thus reducing the risk of toxin dissemination and preserving the quality of uncontaminated food. This approach could minimize food waste and resource inefficiencies linked to discarding significant product amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical Control Points) context could enhance food safety protocols via accurate and prompt detection. By prioritizing prevention, this technology offers a promising chance to optimize quality, efficiency, and sustainability of future food production processes

An economic analysis of a robotic harvest technology in New Zealand fresh apple industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, Massey University School of Agriculture and Environment, Manawatu, New Zealand

The New Zealand apple industry is predominately an export-oriented industry relying on manual labour throughout the year. In recent years, however, labour shortages for harvesting have been jeopardising its competitiveness and profitability. Temporary immigration labour programs, such as the Recognised Seasonal Employer (RSE) program have not been able to solve the labour shortages, urging the industry to consider use of harvesting automation, i.e. robotic technology, as a solution. Harvesting robots are still in commercial trial stage and no studies have assessed the economic feasibility of such technology. The present study for the first time develops a bio-economic model to analyse the investment decision for adopting harvesting robots compared to available alternatives, i.e. platform and manual harvesting systems, using net present value (NPV) as the method of analysis; for newly established single-, bi-, and multi-varietal orchards across different orchard sizes, and three apple varieties (Envy, Jazz, and Royal Gala); and implications of orchard canopy transition and associated sensitivities are considered. The results of the analysis identified fruit value and yield as the key drivers for the adoption of harvesting automation. For relatively low value and or yielding varieties such as Jazz or Royal Gala, robots are less profitable in single-varietal orchard compared to bi-varietal orchard planted with relatively low value and yielding varieties. In a multi-varietal orchard, a relatively high value and high yield variety, such as Envy, is crucial to compensate for the costs incurred for harvesting other varieties using robots or platforms. The greatest potential benefit of utilising harvesting robots was reducing pickers required by an average of 54% for Envy and 48% for each of Jazz and Royal Gala across all orchard sizes compared to manual harvesting; and 7% in average for each of Envy, Jazz, and Royal Gala across all orchard sizes compared to platform harvesting system. This study also identified the break-even price for a robotic harvester in a single-varietal orchard, showed that the break-even prices exceeded the assumed price of the robot, and are highly variable depending on the varietal value and yield, where Envy as a relatively higher value and yielding variety returns a break-even price of $2.92 million compared to relatively lower value and yielding varieties, Jazz with$674,895, and Royal Gala with $689,608. Sensitivity analyses showed that both harvesting speed and efficiency are key parameters in the modelled orchard and positively affected the net returns of the investment and must be considered by researchers and manufacturers. However, for developers and potential adopters of robots, it should be more important that robots operate faster, but not necessarily as more efficient in order to generate a high return while substituting the highest number of pickers and leaving less unharvested fruit on trees in the limited harvesting window. Reducing robot price by 12% and 42% can generate an equivalent level of profit similar to manual or platform harvesting, respectively. Increases in labour wages, and decreases in labour availability and efficiency adversely affected the NPV and profitability outlook of the investment, but NPV was more affected by the decreases in labour efficiency and availability than wage increases. This research has important science and policy implications for policy makers, academics, growers, engineers, and manufacturers. From an economic perspective, for late adopters or those growers who may not be financially able to invest in robots or may be uncertain about their performance, platform harvesting system can be utilised as an alternative solution that is commercially available until robotic harvesting technology improves or becomes more affordable, and commercially available. Alternatively, it may be possible for these orchardists to benefit from utilising the robotic harvester in the form of a co-operative or contract-harvesting business model to avoid the capital costs associated with purchasing and operating the robots. Besides the economic factors, robotic harvesters have the potential to be considered as a solution for non-economic factors such as food safety problems. This is more apparent in the post-Covid-19 pandemic era, which has not only made it more difficult for growers to source their required workers due to border closures, but also has led consumers to be more cautious about food safety when they make purchase decisions and prefer to have their fresh fruit touchless from farm to plate. This may not be a problem for packhouses as most are automated, but it may be an issue for harvesting operations, because pickers have to pick apples by hand. Even though robots cannot be the only option for growers to rely on for the foreseeable future as they are not commercially available, in the current situation robot harvesting may be the most ideal solution

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas