P-AgBot: In-Row &amp; Under-Canopy Agricultural Robot for Monitoring and Physical Sampling

Kim, Kitae; Deb, Aarya

doi:10.1109/lra.2022.3187275

Cited by 10 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Robot ini dilengkapi 2 sensor LIDAR dan kamera sebagai sensor navigasi. Robot telah mampu memonitor kondisi tanaman jagung berdasarkan daunnya [8]. Gan mengembangkan mobile Robot dengan sistem navigasi menggunakan GPS, IMU, LiDAR dan juga kamera Kinect.…”

Section: Pendahuluanunclassified

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Tamami

Hermawan²,

Hanafi³

et al. 2022

eltek

View full text Add to dashboard Cite

Untuk menunjang lahan pertanian yang subur, diperlukan proses penyiraman agar kadar air dalam tanah tetap terjaga. Kegiatan penyiraman yang dilakukan secara manual membutuhkan banyak energi. Selain itu kadar air yang diberikan dengan penyiraman manual tidak dapat terukur secara akurat. Dalam makalah ini, kami mengusulkan penyiraman otomatis dengan robot kartesian tiga aksis untuk lahan dengan ukuran 3 meter x 1.5 meter dengan 171 titik tanam. Kontrol penyiraman berbasis fuzzy agar kadar air yang diberikan bisa akurat. Sebelum penyiraman, rata-rata kelembapan tanah pada lahan tersebut adalah 45.28% dengan nilai minimal 40%, nilai maksimal 50%. Target kelembapan tanah untuk setiap titik adalah 60%. Robot dapat menyiram seluruh titik tanam tanpa campur tangan manusia. Nilai kadar air rata-rata penyiraman adalah 62.10%, dengan nilai minimal 60%, nilai maksimal 65%. To support fertile agricultural land, a watering process is needed so that the water content in the soil is maintained. Watering activities carried out manually require a lot of energy. In addition, the water content given by manual watering cannot be measured accurately. In this paper, we propose automatic watering with a three-axis Cartesian robot for land with a size of 3 meters x 1.5 meters with 171 planting points. Fuzzy based watering control so that the water content given can be accurate. Before watering, the average soil moisture on the land was 45.28% with a minimum value of 40%, a maximum value of 50%. The target soil moisture for each point is 60%. The robot can water the entire planting point without human intervention. The average water content value of watering is 62.10%, with a minimum value of 60%, a maximum value of 65%. In addition, also compared with the application error with the fuzzy method with the on-off method, the fuzzy method is able to produce more accurate watering with an average error rate of 2.10%, while the on-off method has an average error of 5.32% against the soil moisture target. The fuzzy method is also more time efficient in watering, which is 7 seconds to 8 seconds, while the on-off method requires a watering time of 10 seconds to 15 seconds

show abstract

Section: Pendahuluanunclassified

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Tamami

Hermawan²,

Hanafi³

et al. 2022

eltek

View full text Add to dashboard Cite

show abstract

“…Such challenges are difficult to solve with conventional agricultural machinery, which has an important carbon footprint [4]. As a result of the research efforts, there are several robotic solutions addressing several different areas such as monitoring [5], mapping [6], crop and pest managing [7], environmental control [8], phenotyping [9], and planting [10]. For example, the researcher in [11] developed an original LiDAR-based high-throughput phenotyping system for cotton plant phenotyping in the field.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Energy Sources for a Photovoltaic/Fuel Cell Extended-Range Agricultural Mobile Robot

et al. 2023

View full text Add to dashboard Cite

Powertrain electrification in the agricultural vehicles is still in the initial stages. This article analyzes the energy behavior of a Photovoltaic/Fuel Cell Agricultural Mobile Robot (PV/FCAMR) as the preliminary step before development. This concept incorporates three energy storage sources for the powertrain: a battery pack, a Fuel Cell (FC) system, and a Photovoltaic (PV) system. This paper proposes an approach based on the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms to determine the sizes of the FC and battery of an FCAMR. A differential drive mobile robot was used as a case study to extract the typical working cycles of farming applications. The FCAMR vehicle model was developed in MATLAB/Simulink to evaluate vehicle energy consumption and performance. For the energy analysis and evaluation, the FCAMR was tested based on two realistic working cycles comprising circular and rectangular moving patterns. The results showed that the proposed arrangement could extend the FCAMR autonomy by 350% as opposed to the pure electric system. This allows for at least 8 h of work with a tank filled with 150 g hydrogen and a PV system with a 0.5 m2 monocrystalline solar panel. The simulation results have demonstrated the relevance and robustness of this approach in relation to various working cycles. The cost comparison between the theoretical and optimization sizing methods showed at least an 8% decrease for the FCAMR. Furthermore, adding the PV system extended the vehicle’s range by up to 5%. This study provides an optimal solution for energy sources sizing of mobile robots as futuristic agricultural vehicles.

show abstract

“…Meanwhile, the navigation path detection is the prerequisite of the agricultural robot autopilot [4,5] . However, it is also a challenging task to figure out the navigation path due to the complex agricultural environment [6,7] . Hence, a novel method was developed in this study to find the navigation path for the jujube harvester autopilot.…”

Section: Introductionmentioning

confidence: 99%

Novel method of the visual navigation path detection of jujube harvester autopilot based on image processing

Zhang,

Chen,

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

To realize automatic harvesting of the jujube, the jujube harvester was designed and manufactured. For achieving the jujube harvester autopilot, a novel algorithm for visual navigation path detection was proposed. The centerline of tree row lines was taken as the navigation path. The method included four main parts: image preprocessing, image segmentation, tree row lines access, and navigation path access. The methods of threshold segmentation, noise removal, and border smoothing were utilized on the image in Lab color space for the image segmentation. The least square method was employed to fit the tree row lines, and the centerline was obtained as the navigation path. Experimental results indicated that the average false detection rate was 3.98%, and the average detection speed was 41 fps. The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed. It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.

show abstract

P-AgBot: In-Row & Under-Canopy Agricultural Robot for Monitoring and Physical Sampling

Cited by 10 publications

References 21 publications

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Optimal Design of Energy Sources for a Photovoltaic/Fuel Cell Extended-Range Agricultural Mobile Robot

Novel method of the visual navigation path detection of jujube harvester autopilot based on image processing

Contact Info

Product

Resources

About