Corn Classification System based on Computer Vision

Li, Xiaoming; Dai, Baisheng; Sun, Hong-Min; Li, Wei-Na

doi:10.3390/sym11040591

Cited by 32 publications

(20 citation statements)

References 16 publications

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“…Recently, while deep learning techniques have come to dominate much of the computer vision space, Grift et al ( 2017 ), Li et al ( 2019 ), and others continue to use image processing techniques; as their task of interest was highly constrained—often taken in a laboratory setting with a simplistic background, controlled lighting, loose kernels which naturally have separation —the flexibility and generalization offered by deep learning methods may not have warranted the effort of collecting a dataset to support deep learning methods. Even more recently, Wu et al ( 2020 ) used a five-step approach consisting of Gaussian Pyramids, Mean Shift Filtering, Color Deconvolution, local adaptive thresholding, and local maxima finding to count the kernels on an ear of corn.…”

Section: Related Workmentioning

confidence: 99%

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels

Hobbs

Khachatryan²,

Anandan

et al. 2021

Front. Robot. AI

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Crop monitoring and yield prediction are central to management decisions for farmers. One key task is counting the number of kernels on an ear of corn to estimate yield in a field. As ears of corn can easily have 400–900 kernels, manual counting is unrealistic; traditionally, growers have approximated the number of kernels on an ear of corn through a mixture of counting and estimation. With the success of deep learning, these human estimates can now be replaced with more accurate machine learning models, many of which are efficient enough to run on a mobile device. Although a conceptually simple task, the counting and localization of hundreds of instances in an image is challenging for many image detection algorithms which struggle when objects are small in size and large in number. We compare different detection-based frameworks, Faster R-CNN, YOLO, and density-estimation approaches for on-ear corn kernel counting and localization. In addition to the YOLOv5 model which is accurate and edge-deployable, our density-estimation approach produces high-quality results, is lightweight enough for edge deployment, and maintains its computational efficiency independent of the number of kernels in the image. Additionally, we seek to standardize and broaden this line of work through the release of a challenging dataset with high-quality, multi-class segmentation masks. This dataset firstly enables quantitative comparison of approaches within the kernel counting application space and secondly promotes further research in transfer learning and domain adaptation, large count segmentation methods, and edge deployment methods.

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Section: Related Workmentioning

confidence: 99%

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels

Hobbs

Khachatryan²,

Anandan

et al. 2021

Front. Robot. AI

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“…Li et al [27] (corn) 100 96.67 Men [28] To sum up, the proposed system works in four different stages described as follows:…”

Section: Number Of Seed Samples Correct Classification Rate (%)mentioning

confidence: 99%

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Pourdarbani

Kalantari

Hernández-Hernández

et al. 2020

Foods

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Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

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“…Xiaoming Li et al [8] proposed an automated system for classification of seven kinds of corns which includes normal, heat-damaged, germ-damaged, cob rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged. Input corn image was binarized by Ostu's to separate the corn from background followed by segmentation with line profile based segmentation algorithm.…”

Section: Related Workmentioning

confidence: 99%

Mobile App for Grading of Peanuts using Fuzzy Inference System

Karegowda,

2019

IJEAT

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The peanut a globally adopted vegetable protein with appreciable nutritive contents. The grading is one of the significant way to ensure that quality of the peanuts regulate the appropriate price in market. The proposed work is carried out in two phases. As part of first phase segmentation is done using canny edge detection followed by extraction of four Shape Features (SF): Area (Ar), Major Axis (MajAxis), Minor Axis (MinAxis), Perimeter (PeriMt). As part of second phase, Fuzzy Inference System (FIS) has been adopted for grading of four categories of peanuts. .The interval scaled shape features obtained in phase one are converted to Linguistic Terms (LingTerms). The fuzzy data base rules are attained using Association Rules with the constraints that the consequent can have only class label. The proposed work is developed as mobile app which takes peanut image as input and processes the image using FIS and provides the output in terms of pie chart. The pie chart provides the percentage of different qualities of peanuts (low/medium/good/fine) in the input image. The proposed work resulted in an accuracy of 95% when compared with the ground truth. The work can be applied in automation process for grading of food grains using mobile apps, which helps the lay customer to know about the quality of peanuts. The work can also be used for auto separation of peanuts for packing based on grade of the peanuts.

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Corn Classification System based on Computer Vision

Cited by 32 publications

References 16 publications

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Mobile App for Grading of Peanuts using Fuzzy Inference System

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