بررسی اثر آلودگی شیمیایی ناشی از سمپاشی مزرعه بر رفتار زنبور عسل با استفاده از تکنیک های داده کاوی

سمپاشی های صورت گرفته در مزارع برای مبارزه با آفات از جمله مشکلاتی است که زندگی زنبورهای عسل و عملکرد آن ها را تهدید می کند. بنابراین در پژوهش حاضر شرایط درونی کندو با استفاده از تجهیز آن به حسگرهای ارتعاش، دما، رطوبت و دی اکسیدکربن طی مدت 72 ساعت از زمان سم پاشی مزارع بررسی شد. با توجه به آنالیز ضرائب مل، آفت کش سبب افزایش 100 واحدی شدت در محدوده فرکانسی 1800 تا 2200 هرتز شد. به علاوه با توجه به اطلاعات به دست آمده از دیگر حسگرها، دما تحت شرایط نامساعد (وجود آفت کش پریمیکارب (پریمور) wp50% در فضا) نسبت به شرایط نرمال از 35 به 39 درجه سلسیوس، میزان دی اکسید کربن از 450 به 530 پی پی ام و رطوبت حدود 10 درصد افزایش یافت. به منظور طبقه بندی ویژگی های استخراج شده تحت هر دو شرایط (شرایط آلوده به سموم شیمیایی و شرایط بدون آلودگی) ابتدا با استفاده از آنالیز مولفه های اصلی انتخاب ویژگی صورت پذیرفت و 6 مولفه با حداقل خطای میانگین مربعات 078/0 انتخاب شدند. پس از انتخاب ویژگی ها، طبقه بندی ویژگی های منتخب با استفاده از ماشین بردار پشتیبان با کرنل های مختلف ( rbf، خطی، چندجمله ای، کوادراتیک، سیگموئید) انجام شد که کرنل rbf دو شرایط غیرآلوده و آلوده به آفت کش را به ترتیب با 100% و 90% دقت تشخیص داد. به طور کلی از بین حسگرهای مورد استفاده در سامانه هوشمند، حسگر ارتعاش بهترین نتیجه را به منظور تشخیص شرایط نامساعد کندو در بر داشت.

Investigation the effect of chemical pollution caused by field spraying on honeybee behavior using data mining techniques

Introduction Honeybees play an important role in pollination. However, there are many problems that threaten the life of them. Pollinators can be exposed to insecticides during their application, by contact with residues, or from the ingestion of pollen, nectar or guttation fluid containing insecticide. The increasing use of neonicotinoids means there is a greater potential for pollinators to be exposed over longer periods as systemic insecticides can be found in the pollen and nectar of plants throughout their blooming period (Ellis, 2010). Exposure to insecticides may have lethal or sublethal behavioral or physiological effects. The impact of imidacloprid on homing flight was evaluated in field with a 500mdistance between feeder and hive (Bortolotti et al. 2003). At the concentration of 100 lg kg1 foragers fed with imidaclopridadded syrup returned to the hive, but this treatment caused a temporary inhibition of the foraging activity, lasting more than 5 h. Foragers fed with 500 and 1000 lg kg1 of imidacloprid were seen neither at the hive nor at the feeding site, for the 24 h after the treatment (Bortolotti et al. 2003). Decourtye et al (2011) have shown how the RFID device can be used to study the effects of pesticides on both the behavioral traits and the lifespan of bees.In this context, they have developed a method under tunnel to automatically record the displacements of foragers individualized with RFID tags and to detect the alteration of the flight pattern between an artificial feeder and the hive. Fipronil was selected as test substance due to the lack of information on the effects of this insecticide on the foraging behavior of freeflying bees. They showed that oral treatment of 0.3 ng of fipronil per bee (LD50/20) reduced the number of foraging trips. Therefore, the aim of this study was to monitoring and determination honeybee’s behavior in exposure to pesticide using data mining techniques. Materials and Methods Three smart beehive systems developed to monitoring of hive internal conditions. Therefore, each beehive equipped with temperature and humidity (HDC1080, China), vibration (MPU6050, China), and CO2 (CCS811, China) sensors. Data was collected during spraying time for 48 hours and different features of vibration signal in two timefrequency and frequency domains were extracted by MFCC (MelFrequency Cepstral Coefficient) algorithm. After that, the most significant features were selected using PCA (Principle Component Analysis) which has been used specifically for extracting information from correlation matrices. Since the spectral dataforms the array of correlated variables containing overlapped information, this approach makes it possible to extractuseful information from highdimensional data. To choose thenumber of components the crossvalidationmethod was used. The extracted principal components wereused as the input variables for the classification model. In this paper, support vector machine with different kernel function including linear, polynomial, MLP, RBF, and quadratic was applied for performing classification. Results and discussion According to the MFCC of internal vibration results, there were dramatic changes in the range of 1800 to 2200 Hz in the time of spraying; also, Spectrogram of MFCC coefficients for the X component acceleration shown intensity of 350 in the frequency of 2000 Hz and time range of 60 to 120 minutes; besides, humidity (8 to 18 %), the amount of CO2 (450 to 530 ppm) and temperature (35 to 39 C) increased during this time.To reduce the dimensionality of data five PCs with minimum estimated mean squared prediction error (0.078) were selected based on Monte Carlo method and used in classifier. Among the five kernels (RBF, linear, MLP, Polynomial, Quadratic), RBF could recognize normal and infected colony with identification rate of 100% and 90%, respectively. Conclusions According to the results temperature, humidity, CO2, and vibration sensors can recognize internal condition of bee hive. Vibration features of honey bees movements were extracted using MFCC followed by PCA in frequencytime domain. Five PCs was selected by crossvalidation method and RBF kernel was the best kernel with identification rate of 100% and 90% for normal and infected beehive, respectively. Generally, the vibration signals (that were recorded by acceleration sensor) have shown the best result compare to temperature, CO2, and humidity sensors. It is worth nothing that the use of two temperature and humidity sensors is necessary to monitor and control of beehive internal conditions.