Enhancing a flexible robotic spraying platform for distant plant inspection via high-quality thermal imagery data

Loukatos, Dimitrios; Templalexis, Charalampos; Lentzou, Diamanto; Xanthopoulos, G.; Arvanitis, Konstantinos G.

doi:10.1016/j.compag.2021.106462

Cited by 11 publications

(11 citation statements)

References 41 publications

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“…Relatively large errors when detecting the leaf area of very small lettuce plants are observed as well by Lu et al, even when using Mask-RCNN algorithms [72]. In recent years, researchers have achieved good results in small object detection in optical remote sensing images through the improved Faster R-CNN [13].…”

Section: Plant Identification and Localizationmentioning

confidence: 80%

“…Different technologies and automations such as simple integrated circuits to more complex microcontrollers, sensors, micro-computers and Internet of Things (IoT) applications [11][12][13]; have been implemented in agriculture through smart farming applications (SFT) that promote (i) data acquisition, (ii) data analysis-evaluation and (iii) precision application [14], with significant positive impacts in environmental, economic and labor parameters [15,16]. However, these applications are present in large-scale farming, while very limited work has been done in small-scale, automated UA.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of an Urban Farming Robot

2022

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Urban agriculture can be shortly defined as the growing of plants and/or the livestock husbandry in and around cities. Although it has been a common occupation for the urban population all along, recently there is a growing interest in it both from public bodies and researchers, as well as from ordinary citizens who want to engage in self-cultivation. The modern citizen, though, will hardly find the free time to grow his own vegetables as it is a process that requires, in addition to knowledge and disposition, consistency. Given the above considerations, the purpose of this work was to develop an economic robotic system for the automatic monitoring and management of an urban garden. The robotic system was designed and built entirely from scratch. It had to have suitable dimensions so that it could be placed in a balcony or a terrace, and be able to scout vegetables from planting to harvest and primarily conduct precision irrigation based on the growth stage of each plant. Fertigation and weed control will also follow. For its development, a number of technologies were combined, such as Cartesian robots’ motion, machine vision, deep learning for the identification and detection of plants, irrigation dosage and scheduling based on plants’ growth stage, and cloud storage. The complete process of software and hardware development to a robust robotic platform is described in detail in the respective sections. The experimental procedure was performed for lettuce plants, with the robotic system providing precise movement of its actuator and applying precision irrigation based on the specific needs of the plants.

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Section: Plant Identification and Localizationmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Urban Farming Robot

2022

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“…The more data available, the better results are obtained. A dataset can be collected using advanced technologies such as sensors mounted on ground-based platforms, satellites, and unmanned aerial vehicles (UAVs) [41][42][43][44][45][46].…”

Section: Data Acquired and Data Preparationmentioning

confidence: 99%

“…In addition to processing, it can also transmit the processed data wirelessly to neighboring nodes or router nodes using transceiver [47]. Sensors mounted on ground-based platforms are divided into hand-held, free-standing in the field (Figure 9), and agricultural machinery-mounted sensors that can measure soil, plant, and weather characteristics [41,42,48]. The advantages of ground-based sensors are that they are less expensive than other sensors, they are not sensitive to humid conditions, and they are more suitable for small farms.…”

Section: Data Acquired and Data Preparationmentioning

confidence: 99%

“…The unmanned aerial systems can also be expensive, but there are models that are inexpensive. The disadvantages of unmanned aerial systems are the requirement of a human operator [49], limited flight duration, the limited weight of cargo carried, energy consumption [41], and dependence on weather conditions [41,52]. In addition, the processing of data collected by UAS is higher than that of satellite imagery [49].…”

Section: Data Acquired and Data Preparationmentioning

confidence: 99%

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A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

et al. 2022

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Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.

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Novel Biomimicry Techniques for Detecting Plant Diseases

Fadeyibi,

Fadeyibi

2023

Biomimicry Materials and Applications

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Enhancing a flexible robotic spraying platform for distant plant inspection via high-quality thermal imagery data

Cited by 11 publications

References 41 publications

Design and Implementation of an Urban Farming Robot

Design and Implementation of an Urban Farming Robot

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Novel Biomimicry Techniques for Detecting Plant Diseases

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