A portable, low-cost and sensor-based detector on sweetness and firmness grades of kiwifruit

Garlic root cutting is generally performed manually; it is easy for the workers to sustain hand injuries, and the labor efficiency is low. However, the significant differences between individual garlic bulbs limit the development of an automatic root cutting system. To address this problem, a deep learning model based on transfer learning and a low-cost computer vision module was used to automatically detect garlic bulb position, adjust the root cutter, and cut garlic roots on a garlic root cutting test bed. The proposed object detection model achieved good performance and high detection accuracy, running speed, and detection reliability. The visual image of the output layer channel of the backbone network showed the high-level features extracted by the network vividly, and the differences in learning of different networks clearly. The position differences of the cutting lines predicted by different backbone networks were analyzed through data visualization. The excellent and stable performance indicated that the proposed model had learned the correct features in the data of different brightness. Finally, the root cutting system was verified experimentally. The results of three experiments with 100 garlic bulbs each indicated that the mean qualified value of the system was 96%. Therefore, the proposed deep learning system can be applied in garlic root cutting which belongs to food primary processing.

show abstract

“…There are four general timers and two UARTS. They have the advantages of high performance, low power consumption, and low cost [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Yang

Zhang

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…Vis/NIR has progressively been employed for assessing the internal quality of various fruits, including apricot [6], apple [7][8][9], strawberry [10], citrus [11], etc. For kiwifruit, Vis/NIR was widely used to detect the physicochemical characteristics, including soluble solids content (SSC) [12][13][14], dry matter [15], pH [16], and firmness [17]. Additionally, Vis/NIR has been instrumental in tasks such as freshness identification of kiwifruit [18], evaluation of kiwifruit quality at different maturity stages [19], and discrimination of varietal flesh color [20].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Wan,

Yue,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The sweetness is an important parameter for the quality of Cuixiang kiwifruit. The quick and accurate assessment of sweetness is necessary for farmers to make timely orchard management and for consumers to make purchasing choices. The objective of the study was to propose an effective physical method for determining the sweetness of fresh kiwifruit based on fruit hyperspectral reflectance in 400–2500 nm. In this study, the visible and near-infrared spectral (Vis/NIR) reflectance and sweetness values of kiwifruit were measured at different time periods after the fruit matured in 2021 and 2022. The multiplicative scatter correction (MSC) and standard normal variable (SNV) transformation were used for spectral denoising. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods were employed to select the most effective features for sweetness, and then the features were used as the inputs of partial least squares (PLS), least squares support vector machine (LSSVM), back propagation neural network (BP), and multiple linear regression (MLR) models to explore the best way of sweetness predicting. The study indicated that the most sensitive features were in the blue and red regions and the 970, 1200, and 1400 nm. The sweetness estimation model constructed by using the data of the whole harvest period from August to October performed better than the models constructed by each harvest period. Overall results indicated that hyperspectral reflectance incorporated with MSC-SPA-LSSVM could explain up to 79% of the variability in kiwifruit sweetness, which could be applied as an alternative fast and accurate method for the non-destructive determination of the sweetness of kiwifruit. This research could partially provide a theoretical basis for the development of nondestructive instrumentation for the detection of kiwifruit sweetness.

show abstract

“…The hierarchical transmission control system has gradually combined a variety of advanced technologies in the process of automation development. Such as sensors data collection (Yang et al, 2020), communication network (Arakeri, 2016), automatic control (Siddiq et al, 2019) and intelligent judgment (Blasco et al, 2003;Jahanbakhshi et al, 2021), etc., carry out intensive management of agricultural processes. Continuously improve the complexity, stability and reliability of the system.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Apple’s automatic sorting system based on machine learning

et al. 2022

View full text Add to dashboard Cite

In order to reduce post-harvest losses, the classification of fresh apples is crucial. Taking the hierarchical transmission control system as the object, the research was carried out on the verification of the bus network can flexibly expand the motor equipment, the stable and reliable operation of the motor, and the accuracy of Apple's classification. Combine Labview virtual instrument technology to realize the design of Apple's hierarchical transmission control system based on Controller Area Network technology. Fuzzy PID and traditional PID algorithms are used to simulate and realize the operation of brushless DC motor, and compare the advantages of brushless DC motor control based on fuzzy PID to ensure the safe and stable operation of the system. Using the machine learning algorithms model for color detection, the Support Vector Machine algorithm model finally achieved the classification of the three types of apple samples with a recognition rate of 96.7%.

show abstract

A portable, low-cost and sensor-based detector on sweetness and firmness grades of kiwifruit

Cited by 22 publications

References 33 publications

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Implementation of Apple’s automatic sorting system based on machine learning

Contact Info

Product

Resources

About