Grain-Grading Systems

Hulasare, Rajshekhar; Dronzek, Bernie; Jayas, Digvir S.

doi:10.1201/9780203911310.ch3

Cited by 6 publications

(3 citation statements)

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“…Handling, transportation, processing, treatment, and storage operations are significantly influenced by the characteristics of the grain classification system adopted. Grains produced and harvested in the field are physically heterogeneous due to the diversity of the other elements present as plants, insects, inert matter, and soil (Hulasare et al, 2003). Therefore, due to its importance in the grain production chain, it is evident the need for agile and effective methods for its utilization.…”

Section: Discussionmentioning

confidence: 99%

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Patrício

Rieder

2018

Computers and Electronics in Agriculture

700

257

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Grain production plays an important role in the global economy. In this sense, the demand for efficient and safe methods of food production is increasing. Information Technology is one of the tools to that end. Among the available tools, we highlight computer vision solutions combined with artificial intelligence algorithms that achieved important results in the detection of patterns in images. In this context, this work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley. In this sense, we present 25 papers selected in the last five years with different approaches to treat aspects related to disease detection, grain quality, and phenotyping. From the results of the systematic review, it is possible to identify great opportunities, such as the exploitation of GPU (Graphics Processing Unit) and advanced artificial intelligence techniques, such as DBN (Deep Belief Networks) in the construction of robust methods of computer vision applied to precision agriculture.

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Section: Discussionmentioning

confidence: 99%

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Patrício

Rieder

2018

Computers and Electronics in Agriculture

700

257

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“…Identification of seeds, such as wheat, maize, rice, rye, etc., is generally used for better food processing, storage, marketing, healthy crop yield, and disease immunity. [1] Usually, grains of any crop contain different outliers such as stones, weeds, debris, damaged kernels, and inert matters. It is mandatory to separate these outliers for quality assurance.…”

Section: Introductionmentioning

confidence: 99%

Classification of canola seed varieties based on multi-feature analysis using computer vision approach

Qadri

Razzaq

et al. 2021

International Journal of Food Properties

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This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier.

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“…Although different countries established their own systems for classifying wheat on the basis of different quality parameters, wheat grading systems is commonly based on the wheat protein content (Williams, 2007;Hulasare et al, 2003;Váradi et al, 1999). The price of wheat is dependent on the protein content, often with substantial price increments between grades.…”

Section: The Role Of Nirs In Cereal Tradementioning

confidence: 99%