پديد آورندگان :
ﻋﺰﯾﺰﭘﻮر، زﯾﻨﺐ دانشگاه رازي - دانشكده كشاورزي -گروه گياهپزشكي، كرمانشاه، ايران , واﺣﺪي، ﺣﺴﻨﻌﻠﯽ دانشگاه رازي - دانشكده كشاورزي -گروه گياهپزشكي، كرمانشاه، ايران , ﻟﺮﺳﺘﺎﻧﯽ، ﻋﻠﯽ ﻧﺠﺎت دانشگاه رازي - دانشكده كشاورزي - گروه مهندسي مكانيك بيوسيستم، كرمانشاه، ايران
كليدواژه :
ﭘﺮدازش ﺗﺼﻮﯾﺮ , زﻧﺠﺮك ﭘﺴﺘﻪ , ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ , ﺷﻨﺎﺳﺎﯾﯽ
چكيده فارسي :
اﺳﺘﺮاﺗﮋي ﻣﺪﯾﺮﯾﺖ ﺗﻠﻔﯿﻘﯽ آﻓﺎت )IPM(، ﺑﻪ ﭘﺎﯾﺶ ﭘﯿﻮﺳﺘﻪ ﺟﻤﻌﯿﺖ آﻓﺎت واﺑﺴﺘﻪ اﺳﺖ، اﯾﻦ ﮐﺎر ﻧﻪ ﺗﻨﻬﺎ زﻣﺎن ﺑﺮ اﺳﺖ، ﺑﻠﮑﻪ واﺑﺴﺘﮕﯽ زﯾـﺎدي ﺑـﻪ داور ي اﻧﺴﺎن دارد و ﭘﺮ ﻫﺰﯾﻨﻪ ﻧﯿﺰ ﻣﯽ ﺑﺎﺷﺪ. اﺳﺘﻔﺎده از روش ﻫﺎي ﻫﻮش ﻣﺼﻨﻮﻋﯽ ﺑﻪ ﺟﺎي ﺗﺼﻤﯿﻢ ﮔﯿﺮي ﻫﺎي دﺳﺘﯽ و اﻧﺴﺎﻧﯽ، ﻋﻼوه ﺑـﺮ اﯾـﻦ ﮐـﻪ ﺳـﺒﺐ اﻓـﺰ اﯾﺶ ﺑﻬﺮه وري ﻣﯽ ﮔﺮدد، از دﻗﺖ ﺑﺎﻻﯾﯽ ﻧﯿﺰ ﺑﺮﺧﻮردار اﺳﺖ. ﭘﺴﺘﻪ، ﯾﮏ ﻣﺤﺼﻮل ﺗﺠﺎري اﺳﺖ و ﻫﺮ ﺳﺎﻟﻪ ﺧﺴﺎرت زﯾﺎدي ﺗﻮﺳﻂ ﺣﺸﺮات ﺑـﻪ ﺗﻮﻟ ﯿﺪﮐﻨﻨـﺪﮔﺎن اﯾـﻦ ﻣﺤﺼﻮل وارد ﻣﯽ ﺷﻮد. ﮔﺮوﻫﯽ از آﻓﺎت ﭘﺴﺘﻪ ﻋﻤﺪﺗﺎً از ﻣﯿﻮه ﭘﺴﺘﻪ ﺗﻐﺬﯾﻪ ﻣﯽ ﮐﻨﻨﺪ، ﮐﻪ از اﯾﻦ ﮔﺮوه زﻧﺠﺮك ﭘﺴﺘﻪ، داراي اﻫﻤﯿﺖ زﯾـﺎدي ﻣـﯽ ﺑﺎﺷـﺪ . در اﯾـﻦ ﺗﺤﻘﯿﻖ زﻧﺠﺮك ﭘﺴﺘﻪ ﺑﻪ ﻋﻨﻮان ﺣﺸﺮه ﻫﺪف ﺟﻬﺖ ﺷﻨﺎﺳﺎﯾﯽ اﻧﺘﺨﺎب ﺷﺪ. ﺑﺮاي ﺟﻤﻊ آوري ﻧﻤﻮﻧﻪ ﻫﺎ از ﮐﺎرت ﻫﺎي زرد ﭼﺴﺒﻨﺪه اﺳﺘﻔﺎده ﺷﺪ. 357 ﺧﺼﻮﺻـ ﯿﺖ رﻧﮕﯽ و 20ﺧﺼﻮﺻﯿﺖ ﺷﮑﻠﯽ ﺑﺮاي ﺷﻨﺎﺳﺎﯾﯽ زﻧﺠﺮك ﭘﺴﺘﻪ ﺑﻪ وﺳﯿﻠﻪ اﻟﮕﻮرﯾﺘﻢ ﭘﺮدازش ﺗﺼﻮﯾﺮ اﺳﺘﺨﺮاج ﺷﺪ. ﺧﺼﻮﺻﯿﺎت رﻧﮕﯽ ﺑﻪ دو دﺳـﺘﻪ ي ﺧﺼﻮﺻـ ﯿﺎت ﻣﺮﺑﻮط ﺑﻪ ﻣﯿﺎﻧﮕﯿﻦ و اﻧﺤﺮاف ﻣﻌﯿﺎر و ﺧﺼﻮﺻﯿﺎت ﻣﺮﺑﻮط ﺑﻪ ﺷﺎﺧﺺ ﻫﺎي ﺳﺒﺰي ﺗﻘﺴﯿﻢ ﺷﺪﻧﺪ. از 17 ﻓﻀﺎي رﻧﮕﯽ ﻣﺨﺘﻠﻒ ﻣﺜﻞ HSV ،RGB و ﻏﯿﺮه ﺑﺮاي اﺳﺘﺨﺮاج ﺧﺼﻮﺻﯿﺎت و از روش ﻫﯿﺒﺮﯾﺪ ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ-اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪ ﺳـﺎز ي ازدﺣـﺎم ذرات ANN-PSO)( ﺑـ ﺮاي اﻧﺘﺨـﺎب ﺧﺼﻮﺻـ ﯿﺎت ﻣـﺆﺛﺮ اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﺧﺼﻮﺻﯿﺎت ﻣﺆﺛﺮ اﻧﺘﺨﺎﺑﯽ ﺟﻬﺖ ﻃﺒﻘﻪ ﺑﻨﺪي ﺣﺸﺮات ﻋﺒﺎرﺗﻨﺪ از: ﺷﺎﺧﺺ رﻧﮕﯽ ﺑﺮاي ﭘﻮﺷﺶ ﮔﯿﺎﻫﯽ اﺳـﺘﺨﺮاﺟ ﯽ ﻣﺮﺑـﻮط ﺑـﻪ ﻓﻀـ ﺎي رﻧﮕـ ﯽ HSL، ﺷﺎﺧﺺ ﺗﻔﺎﺿﻞ ﻧﺮﻣﺎل ﺷﺪه ﻣﺮﺑﻮط ﺑﻪ ﻓﻀﺎي رﻧﮕﯽ LCH، ﮐﺎﻧﺎل ﺧﺎﮐﺴﺘﺮي ﻣﺮﺑﻮط ﺑﻪ ﻓﻀﺎي رﻧﮕﯽ YCbCr، ﺷﺎﺧﺺ ﻣﺆﻟﻔﻪ دوم ﻣﻨﻬﺎي ﻣﺆﻟﻔـﻪ ﺳﻮم ﻣﺮﺑﻮط ﺑﻪ ﻓﻀﺎي رﻧﮕﯽ YCbCr، ﻣﺴﺎﺣﺖ و ﻣﯿﺎﻧﮕﯿﻦ ﻣﺆﻟﻔﻪ ﻫﺎي اول، دوم و ﺳﻮم ﻓﻀﺎي رﻧﮕـ ﯽ Luv. ﻧـﺮخ ﺷﻨﺎﺳـﺎﯾﯽ اﻟﮕـﻮر ﯾﺘﻢ ﭘـﺮدازش ﺗﺼـﻮ ﯾﺮ ﻃﺮاﺣﯽ ﺷﺪه، 99/72درﺻﺪ ﮐﻞ اﺷﯿﺎ )زﻧﺠﺮك ﭘﺴﺘﻪ، ﺳﻮﺳﮏ ﭼﻮب ﺧﻮار ﻗﺮﻣﺰ ﭘﺴﺘﻪ و ﺳﺎﯾﺮ ﺣﺸﺮه ﻫﺎي ﻏﯿﺮ ﻫﺪف و ﻣﺘﻔﺮﻗﻪ( ﻣﯽ ﺑﺎﺷـﺪ . ﺷـﺒﮑﻪ ﻫـ ﺎي ﻋﺼـﺒ ﯽ ﻣﺼﻨﻮﻋﯽ، ﺗﻮاﻧﺎﯾﯽ ﻃﺒﻘﻪ ﺑﻨﺪي ﺣﺸﺮات ﺑﻪ ﺳﻪ ﮐﻼس )زﻧﺠﺮك ﭘﺴﺘﻪ، ﺳﻮﺳﮏ ﭼﻮب ﺧﻮار ﻗﺮﻣﺰ ﭘﺴﺘﻪ و ﺳﺎﯾﺮ ﺣﺸﺮه ﻫﺎي ﻏﯿﺮ ﻫـﺪف و ﻣﺘﻔﺮﻗـﻪ ( و دو ﮐـﻼس زﻧﺠﺮك ﭘﺴﺘﻪ و ﺳﻮﺳﮏ ﭼﻮب ﺧﻮار ﻗﺮﻣﺰ ﭘﺴﺘﻪ( ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺎ دﻗﺖ 91/53 و 99/59 درﺻﺪ را دارﻧﺪ.
چكيده لاتين :
Pistachio or Green Gold is one of the most important agricultural crops and is especially important for Iranian exports. A group of pistachio's pests mainly feed on pistachio, among which Idiocerus stali is very important. Conventional methods for identifying insects using identification keys are time-consuming and costly. Due to the rapid development of the Pistachio industry, the use of artificial intelligence techniques such as image processing, for identification and population monitoring is highly recommended. On the other hand, little research was carried out on I. stali. Therefore, in this research, I. stali was selected as a target insect for the identification and counting on sticky yellow cards using image processing techniques and artificial neural networks. The purpose of this study was to determine the feasibility of I. stali identification algorithm by image processing, to determine the possibility of separation and counting of I. stali from other non-target insects by artificial neural network and to determine its accuracy in identification of I. Stali.
Materials and Methods
Idiocerus stali was selected as the target insect for identification. Sticky yellow cards were used for collecting samples. Taking the photos with the help of a SONY Handycam Camera, which had a 12-megapixel resolution and G lens, was carried out (SONY, HDR-XR500, CMOS, SONY Lens G, Made in Japan). Then insects were counted on each card manually and the data was recorded. The data, which were digital images of yellow sticky cards, were imported into the MatLab R2017b software environment. A total of 357 color properties and 20 shape's features for the identification of I. stali were extracted by an image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. An ANN-PSO (Artificial Neural Network hybrid method-Particle Swarm Optimization) algorithm was used to select the effective features. The selected effective characteristics for insect classification were: Color index for extra collective vegetation related to HSL color space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, second and third components of color space Luv.
Results and Discussion
Comparing the results with the results of Qiao et al. (2008), we found that in his study, which divided the data into three categories, for medium and high-density groups, the detection rate was 95.2% and 94.6%, respectively. On the other hand, in low densities (less than 10 trapped insects); its detection rate was 72.9%, while the detection rate of the classifier system designed in this study for different densities of trapped insects, was identical and equal to 99.59%. Also, comparing the results of this study with Espinoza et al. (2016), we found that their algorithm in whiteflies detection had a high accuracy of about 0.96 on a sticky yellow card, while the Thrips identification algorithm accuracy was 0.92 on a sticky blue card. As stated above, the correct detection rate of I. stali by the algorithm designed in this study was 99.72%.
Conclusion
The results showed the feasibility of the new method for identifying the pest insects without destroying them on the farm and in natural light conditions and in a short time and with very high accuracy. This suggests that this algorithm can be applied to the machine vision system and can be used in future in the construction of agricultural robots.
