Recognition of Secale Cereal L Weed from Potato Plant using Video Processing and Computational Intelligence

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SABZI S.; ABBASPOUR GILANDEH Y.; Javadi Kia H.

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Journal: Agricultural Mechanization and Systems Research Year:2019 | Volume:20 | Issue:72 Page(s): 1-18

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Information Journal Paper

Title

Recognition of Secale Cereal L Weed from Potato Plant using Video Processing and Computational Intelligence

Author(s)

SABZI S. | ABBASPOUR GILANDEH Y. | Javadi Kia H. | Issue Writer Certificate

Keywords

Machine Vision

Meta-Heuristic Algorithms

Precision Farming

Video Processing

Weeds

Abstract

The Weeds must be removed from the field due to their competition with principal crops to use water, nutrients, sunlight, etc. There are different methods to remove the Weeds: mechanically, manually or chemically (applying herbicides). For farmers, applying herbicides is a usual way, but brings some concerns, from the point of environmental issues, due to equal application of chemicals all over fields, regardless the presence or absence of weed. For this reason, a Machine Vision system based on Video Processing was proposed to recognize Secale cereale L. (as a weed) from potato plant (as principal crop) to make herbicide application more accurate. Nine hundred sixty five objects were recognized after taking videos, pre-processing and segmentation. Fourteen features were extracted from each object. Using the hybrid artificial neural network-genetic algorithm, of 14 extracting features, only 6 features were selected as effective ones: average, the third moment, autocorrelation, correlation, dissimilarity, and entropy. Data were classified into two groups: training data (70% of the total data) and testing data (30% of the total data). The classification was performed using hybrid of artificial neural network-Bio-geography Based Optimization (BBO) algorithm. Performance of classification system was evaluated through analysis of confusion matrix and Receiver Operating Characteristic (ROC). Sensitivity, specificity, and accuracy were calculated using confusion matrix. The results showed that the sensitivity, accuracy and specificity of classification system reached to an acceptable level: 99. 49 %, 99. 65% and 98. 91%, respectively. Our conclusion is that it is possible to manufacture the Machine Vision system with mentioned aims that work as online.

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APA: Copy

SABZI, S., ABBASPOUR GILANDEH, Y., & Javadi Kia, H.. (2019). Recognition of Secale Cereal L Weed from Potato Plant using Video Processing and Computational Intelligence. AGRICULTURAL MECHANIZATION AND SYSTEMS RESEARCH (JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH), 20(72 ), 1-18. SID. https://sid.ir/paper/382885/en

Vancouver: Copy

SABZI S., ABBASPOUR GILANDEH Y., Javadi Kia H.. Recognition of Secale Cereal L Weed from Potato Plant using Video Processing and Computational Intelligence. AGRICULTURAL MECHANIZATION AND SYSTEMS RESEARCH (JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH)[Internet]. 2019;20(72 ):1-18. Available from: https://sid.ir/paper/382885/en

IEEE: Copy

S. SABZI, Y. ABBASPOUR GILANDEH, and H. Javadi Kia, “Recognition of Secale Cereal L Weed from Potato Plant using Video Processing and Computational Intelligence,” AGRICULTURAL MECHANIZATION AND SYSTEMS RESEARCH (JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH), vol. 20, no. 72 , pp. 1–18, 2019, [Online]. Available: https://sid.ir/paper/382885/en

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