AE—Automation and Emerging Technologies

Visen, N.S.; Paliwal, Jitendra; Jayas, D. S.; White, N. D. G.

doi:10.1006/bioe.2002.0064

Cited by 48 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Training of the network was continued until the test error reaches the determined tolerance value. After training of the network ended successfully, the network was tested by test data [9].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…ANN is quite effective and successful while working with non-linear and indefinite data. Therefore, it has a significant potential for classification and identification of agricultural products [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Paliwal et al [12], using the physical properties of grains, studied the classification successes of various ANN structures. Visen et al [9] classified 5 grain species using image processing and ANN techniques. They found the classification successes as follows: in barley 98.7%, in spring wheat 99.3%, in durum wheat 96.7%, in oat 98.4% and ANN is widely used in the classification of grain products.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Varieties of Grain Species by Artificial Neural Networks

et al. 2018

View full text Add to dashboard Cite

In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Varieties of Grain Species by Artificial Neural Networks

et al. 2018

View full text Add to dashboard Cite

show abstract

“…One of the negative aspects is the time required to carry out such assessments, which impedes rapid decision-making and large-scale evaluation . However, due to the natural variability of the products, the task of identification and classification is extremely challenging and computationally intensive because it involves a large number of data due to the number of parameters to be used in the classification (Visen et al, 2002).…”

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

702

257

View full text Add to dashboard Cite

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.

show abstract

“…N.S. Visen et al (2002) compared the accuracy of different architectures of simple and specialist neural networks in the classification of cereals. Morphological and chromatic characteristics of wheat, barley, oat, and rye kernels calculated using color images captured with a CCD camera were used as the input data.…”

Section: Analysis Of Color Characteristics Of Kernelsmentioning

confidence: 99%

Analysis of color and texture characteristics of cereals on digital images

2020

View full text Add to dashboard Cite

The color of the grain shell of cereals is an important feature that characterizes the pigments and metabolites contained in it. The grain shell is the main barrier between the grain and the environment, so its characteristics are associated with a number of important biological functions: moisture absorption, grain viability, resistance to pre-harvest germination. The presence of pigments in the shell affects various technological properties of the grain. Color characteristics, as well as the appearance of the grain shell are an important indicator of plant diseases. In addition, the color of the grains serves as a classifying feature of plants. Genetic control of the color formation of both grains and other plant organs is exerted by genes encoding enzymes involved in the biosynthesis of pigments, as well as regulatory genes. For a number of pigments, these genes are well understood, but for some pigments, such as melanin, which causes the black color of grains in barley, the molecular mechanisms of biosynthesis are still poorly understood. When studying the mechanisms of genetic control of grain color, breeders and geneticists are constantly faced with the need to assess the color characteristics of their shell. The technical means of addressing this problem include spectrophotometers, spectrometers, hyperspectral cameras. However, these cameras are expensive, especially with high resolution, both spatial and spectral. An alternative is to use digital cameras that allow you to get high-quality images with high spatial and color resolution. In this regard, recently, in the field of plant phenotyping, methods for evaluating the color and texture characteristics of cereals based on the analysis of two-dimensional images obtained by digital cameras have been intensively developed. This mini-review is devoted to the main tasks related to the analysis of color and texture characteristics of cereals, and to methods of their description based on digital images.

show abstract

AE—Automation and Emerging Technologies

Cited by 48 publications

References 14 publications

Classification of Varieties of Grain Species by Artificial Neural Networks

Classification of Varieties of Grain Species by Artificial Neural Networks

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

Analysis of color and texture characteristics of cereals on digital images

Contact Info

Product

Resources

About