A Novel Sensor for Fruit Ripeness Estimation Using Lithography Free Approach

Maharshi, Vikram; Sharma, Sumit; Prajesh, Rahul; Das, Samaresh; Agarwal, Ajay; Mitra, Bhaskar

doi:10.1109/jsen.2022.3210439

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…With the development of AI, sensor technology, and computer vision technology, research has been conducted to apply these technologies to the agricultural field, such as smart farms, to create high added-value [28][29][30][31][32][33][34][35]. In particular, for the system that determines the ripeness of fruits, many studies have been carried out utilizing AI and computer vision technology, as it is often necessary to judge the ripeness by external factors such as color, shape, etc.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Park,

Shin,

Kim

et al. 2024

Electronics

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Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment.

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Section: Related Workmentioning

confidence: 99%

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Park,

Shin,

Kim

et al. 2024

Electronics

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“…This approach demonstrated high-accuracy results for apples (96%) and strawberries (83%), indicating the potential of soft tactile sensors in automated fruit quality control systems. A capacitive sensor was also used by Maharshi et al [10]. The flexible nano-needle patterned polydimethylsiloxane (PDMS) works as a dielectric layer to detect the ripeness of the fruits.…”

Section: Food Item Feature Extractionmentioning

confidence: 99%

“…Tactile sensing in the agri-food sector plays a crucial role in the harvesting, manipulation, and feature extraction of food items. Initial research primarily aimed at utilising tactile sensing for determining key features, such as hardness, in order to estimate food ripeness [3][4][5][6][7][8][9][10][11][12]. This enables the classification of food items, particularly in high-occlusion scenarios, where visual cues may be obstructed by a robot's end effector, surrounding environment, or packaging [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Mandil,

Rajendran,

Nazari

et al. 2023

Sensors

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Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

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“…Flexible pressure sensors have significant potential for application in electronic skin, , health and medical monitoring, − human–computer interaction, − and wearable electronic devices. , Based on the sensing mechanism, these sensors can be classified into four types: capacitive, − piezoelectric, , triboelectric, , and resistive. − Resistive pressure sensors have gained attention due to their simple manufacturing process and easy data acquisition and reading. Sensitivity is a crucial performance parameter of the sensor, as it determines the sensor’s ability to perceive pressure signals.…”

Section: Introductionmentioning

confidence: 99%

High-Sensitivity Carbon Nanofibers/Graphene/Polydimethylsiloxane Flexible Pressure Sensor Based on a Hierarchical Structure with Enhanced Sensing Range

Chang,

Dong,

Zhao

et al. 2024

ACS Appl. Electron. Mater.

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Achieving a high sensitivity of sensors over a wide linear range is crucial for practical applications. Introducing various micronano topologies is the most effective approach to enhance sensor sensitivity. Unfortunately, due to the size effect, the surface structure is prone to deformation and saturation under pressure, limiting sensitivity to a narrow range and hindering broader applications. Additionally, few of the recently developed sensors with wide sensing ranges have been able to achieve the high sensitivity levels provided by micronano topological structures. The achievement of an effective trade-off between high sensitivity and a wide sensing range still presents significant challenges. In this study, a versatile strategy is proposed to design a high-sensitivity flexible pressure sensor with an improved sensing range. A cost-effective and adjustable wire mesh is utilized to introduce microstructures while constructing a hierarchically reinforced structure, effectively combining porous and surface microstructures. Hybrid carbon nanofibers and graphene serve as conductive materials to further enhance the performance. The sensor demonstrates a high sensitivity of −1.12 kPa–1 and extends the sensing range to nearly 3.9 times (0–853 Pa) compared to sensors with only microstructures (0–220 Pa). Moreover, it exhibits a response speed comparable to that of the human body (26 and 33 ms) and a high durability (4000 cycles). The sensor showcases excellent signal response to high-pressure movements (finger bending and wrist bending) and low-pressure breathing movements, holding promising potential for motion monitoring and information encryption applications.

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A Novel Sensor for Fruit Ripeness Estimation Using Lithography Free Approach

Cited by 10 publications

References 47 publications

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

High-Sensitivity Carbon Nanofibers/Graphene/Polydimethylsiloxane Flexible Pressure Sensor Based on a Hierarchical Structure with Enhanced Sensing Range

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