Predicting the ripening of papaya fruit with digital imaging and random forests

Pereira, L.F.; Barbon, Sylvio; Valous, Nektarios A.; Barbin, Douglas Fernandes

doi:10.1016/j.compag.2017.12.029

Cited by 143 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most work to date has focused on maturity analysis of fruit that ripen in a uniform fashion, such as tomato [ 32 , 33 , 34 ], passion fruit [ 27 ], apricot [ 24 ], persimmon [ 35 ], blueberry [ 36 , 37 ], cherry [ 38 ], and date [ 39 ]. Different methods were used for classification (e.g., support vector machines [ 27 , 36 ], convolutional neural networks [ 34 , 39 ], random forest [ 40 ], K-nearest neighbor [ 33 ], and linear discriminant analysis [ 35 ]) based on different sensors (e.g., RGB—Red Green Blue [ 29 , 33 , 35 , 36 ], RGB-D—Red Green Blue-Depth [ 27 ], and NIR—Near Infra-Red [ 38 ]). The current research used a RGB camera and focused on the maturity level classification of sweet peppers, which have a nonuniform ripening pattern [ 21 , 23 ] Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

View full text Add to dashboard Cite

The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

show abstract

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The developing field of machine learning is currently being applied to pixel feature extraction, with early applications to image processing, including neural networks [26,27], support vector machines [28,29], decision trees [30,31], and random forests [32,33]. These methods use pixel spectral information as inputs and achieve the desired feature results through complex calculations.…”

Section: Introductionmentioning

confidence: 99%

Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

et al. 2020

View full text Add to dashboard Cite

When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery.

show abstract

“…The development of machine learning has allowed researchers to use machine learning abilities to improve pixel feature extraction. However, early machine learning methods such as neural networks [26,27], support vector machines [28,29], decision trees [30,31], and random forests [32,33] still use pixel spectral information as input. Although these methods can be effective at obtaining features, these remain single-pixel features, without utilizing the spatial relationships between adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Zhang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

show abstract

Predicting the ripening of papaya fruit with digital imaging and random forests

Cited by 143 publications

References 35 publications

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Contact Info

Product

Resources

About