• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.679-684
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• 1996

The work was aimed at realize the problem of seed processing . Solving this problem it was ascertained that the existing mathematical methods are reliable enough, but they are used practically very seldom. The work offers to use the expert system technology which allows to solve problems connected with practical knowledge of experts in the region of investigation effectively. The method of knowledge structuring and analizing as well as technique of knowledge acquisition which is necessary for realization of this technology are worked-out in the work. As the result applying the worked-out method the prototypes of the expert system (ES) are created : -ES " Sieves " ; research prototype for the sieve choice for the seed sorting machines -ES " Diagnostics " ; displaying prototype for the technological determination of action disrepair of seed sorting machines.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.