Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Pitaya Harvesting

Chen, Liang-Bi; Huang, Xiang-Rui; Chen, Wei-Han

doi:10.1109/jsen.2023.3270844

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…This suggests a move towards comprehensive, data-driven decision-making in agriculture. IoT-enabled smart irrigation systems [120][121][122] are another significant trend in crop production. These systems utilize sensors to monitor soil moisture levels and climate conditions, enabling precise and automated irrigation.…”

Section: Crop Productionmentioning

confidence: 99%

“…One potential research gap lies in the development of automated harvesting systems tailored to specific crops. While some studies explore autonomous harvesting robots for crops like soybeans [144] or pitayas [120], there is room for further research into the scalability and adaptability of such systems to various agricultural contexts.…”

Section: Harvest Stagementioning

confidence: 99%

“…Techniques such as data augmentation and transfer learning further optimize AI/ML performance, potentially reducing the burden on IoT devices to generate excessive amounts of data [189,223]. Additionally, the non-physical nature of AI/ML models enables seamless modification and duplication across multiple devices, streamlining deployment and management within IoT ecosystems [120,232].…”

Section: Impact Of Ai/ml Strengths On Iotmentioning

confidence: 99%

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IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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Section: Crop Productionmentioning

confidence: 99%

Section: Harvest Stagementioning

confidence: 99%

Section: Impact Of Ai/ml Strengths On Iotmentioning

confidence: 99%

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IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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“…Studies on visual navigation in environments such as apple orchards [4], cucumber fields [5], orange groves [6], wolfberry plantations [7], peach orchards [8], and jujube orchards [9] have been conducted. The precise and instantaneous identification of navigation paths is a significant area of interest in global research [10,11].…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Zhu,

Deng,

Lai

et al. 2024

Agronomy

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Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

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Machine Learning Methodologies, Wages Paid and the Most Relevant Predictors

Martinho

2024

SpringerBriefs in Applied Sciences and Technology

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Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Pitaya Harvesting

Cited by 15 publications

References 24 publications

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Machine Learning Methodologies, Wages Paid and the Most Relevant Predictors

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