Application of neural image analysis in evaluating the quality of greenhouse tomatoes

Zaborowicz, Maciej; Boniecki, P.; Koszela, Krzysztof; Przybylak, Andrzej; Przybył, J.

doi:10.1016/j.scienta.2017.02.001

Cited by 57 publications

(21 citation statements)

References 21 publications

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“…The capability of holistic feature extraction of CNN has reported significant object classification effectiveness. Currently, Neural Networks (NN) has gained a significant importance in the food industry [2,13,29,45,72,180,203]. One of the constraints of CNN is the scarcity of substantial dataset for training the CNN.…”

Section: Classificationmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

150

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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Section: Classificationmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

150

View full text Add to dashboard Cite

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“…The identification and recognition of pests should therefore take place before the grain is taken to silos. This procedure must be quick, efficient, and precise, which implies a necessity to automate this identification process [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

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“…More and more often, particular stages of agricultural production are supported by modern technologies (such as computer image analysis and signal analysis) or are replaced by fully-automated product evaluation systems [9,10]. This is due to the pursuit of a high level and repeatability of the assessment.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

et al. 2020

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This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.

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Application of neural image analysis in evaluating the quality of greenhouse tomatoes

Cited by 57 publications

References 21 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

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