A general purpose multi-fruit system for assessing the quality of fruits with the application of recurrent neural network

Dhiman, Bhumica; Kumar, Yogesh; Hu, Yu‐Chen

doi:10.1007/s00500-021-05867-2

Cited by 18 publications

(8 citation statements)

References 35 publications

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“…Time series analysis such as stock price forecasting, speech recognition, and sentiment are some of the general use cases (Dilmegani, 2023). In the pre-processing stage, (Dhiman, Kumar, and Hu, 2021) used a contrast enhancement technique, followed by grayscale conversion to balance the unstable light in the input fruit image suppressing the object definition. Subsequently, canny edge detection was used to discover the boundaries of the fruits, and quality assessment was performed with an accuracy of 98.47% by using RNN.…”

Section: Rnnmentioning

confidence: 99%

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A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry

Naghipour,

Ling,

Connie

2024

IJTech

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Artificial Intelligence (AI) techniques are used in agricultural industry for detecting, classifying, and assessing the quality of fruits. The primary focus of the discussed fruits pertains to oil palm fresh fruit bunches (FFB), which have been a significant contributor to Malaysia's economy. The quantity of research concerning oil palm FFB is limited and has not received extensive attention in the literature. A clear guide regarding the most useful types of data and features in the field is absent. Different concerns also persist regarding the ability of AI models to perform agricultural tasks with sufficient accuracy. Therefore, this review aims to explore the significant data, features, and AI models used, ascertain the performance level in the domain, and contribute an informative analysis of agricultural and oil palm fields. In this context, various types of data, capturing devices, public datasets, features, and diverse AI models used in agricultural industry are subjected to analysis. Most of the analyzed research achieved above 90% performance in terms of accuracy, coefficient of determination (R2), as well as sensitivity and mean average precision (mAP). The results show that there is a high capability of AI to perform agricultural tasks with high accuracy. In this context, the literature is thoroughly examined to provide a comprehensive understanding of the different elements of AI in agricultural industry.

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Section: Rnnmentioning

confidence: 99%

“…(Xu et al, 2021) proposed a machine learning technique, using a Random Forest (RF) algorithm based on improved grid search optimization (IGSO-RF) to detect oil palm plantations. Meanwhile, fruit quality detection has been performed with deep learning when a successful cross was achieved (Dhiman, Kumar, and Hu, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry

Naghipour,

Ling,

Connie

2024

IJTech

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“…With the development of miniaturization and information intelligence in IoT, sensor development also tends to be more and more intelligent, which promotes the development of data acquisition technology in industrial IoT [10]. Dhiman B et al [11] designed a multifrequency machine vision nondestructive MWM array sensor, and experiments showed that the sensor has good detection capability for fatigue damage of steel [11]. Khan S et al designed a CODFCI sensor, which combined a detection coil with a high-resolution charge-coupled device to make a probe.…”

Section: Key Technologymentioning

confidence: 99%

“…P(x) represents the speedup ratio of a parallel system, which is defined as the ratio of the single-computer computation time P(t + 1) to the multicomputer computation time P(t) for parallel computing problems. e equation for calculating the speedup ratio under a fixed-size parallel system is shown in (11). P is the size of the parallel computing system.…”

Section: Monitoring Equipment Manual Supervision Systemmentioning

confidence: 99%

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

Wang

Liu

2022

Mathematical Problems in Engineering

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This paper introduces the development status of machine vision nondestructive testing technology and industrial IoT supervision mechanism. The study designs and implements a machine vision nondestructive testing system from two aspects: construction of industrial IoT supervision and detection model, and optimization of machine vision nondestructive testing algorithm. In this paper, the random deployment of dynamic and static nodes is adopted. The coverage rate after random deployment and the moving distance of dynamic nodes are two necessary research parameters. To improve the initial coverage and optimize the mobile path of dynamic nodes, this paper proposes a mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network. The optimized machine vision nondestructive detection algorithm is used for mobile path optimization from dynamic nodes to target locations. Simulation results show that a random deployment of 100 static nodes and 20 dynamic nodes in a 400 m × 400 m factory area works best with a coverage rate of 6.719% and an average movement distance of 23.47 m, and the movement path avoids the obstacle area. The average accuracy of the modified machine vision nondestructive testing system is 1.59% higher than that before the modification, and the average detection accuracy of the final experiment reaches 95.46%. Not only is the coverage rate better than that of the cellular structure-based dynamic node optimization scheme, but also the monitoring range of the plant tends to be more comprehensive in the actual deployment environment. Through the analysis of test results, the system achieves the monitoring and display of data on the one hand and provides a natural information access and interaction experience for IoT managers on the other hand, which meets the requirements of real time, accuracy, and stability of industrial IoT information data to a certain extent.

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“…Examples of different fruit harvesting robots. ; (a) Dual arm Tomato Harvesting Robot with SCARA like Manipulator 47 , (b) Sweet Pepper Harvesting Robot 47 Harvey platform, (c) Sweet pepper harvesting Robot 45 , (d) Thorvald II - Single rail based cartesian type multiarm system 172 , (e) Thorvald II - Strawberry harvesting with cabel driven gripper 173 , (f) Kiwifruit Harvesting Robot 12 , (g) Apple Harvesting Robot with UR5 Manipulator 11 , (h) Apple Harvesting Robot 174 , (i) Humanoid Apple 175 and Grape 55 harvesting robot .…”

Section: Fruit Harvesting Robotsmentioning

confidence: 99%

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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A general purpose multi-fruit system for assessing the quality of fruits with the application of recurrent neural network

Cited by 18 publications

References 35 publications

A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry

A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

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