امکان‌سنجی تعیین بافت خاک با استفاده از پردازش سیگنال‌های صوتی یک نفوذسنج مخروطی

بافت خاک یکی از خصوصیات مهم خاک است که با بسیاری از جنبه‌های عملکرد خاک، از بهره‌وری تا سهولت خاک‌ورزی، در ارتباط است. در حال حاضر، تعیین بافت خاک به دو صورت انجام می‌گیرد: در سطح مزرعه با دقت کم یا در محیط آزمایشگاهی و زمان‌بر. در این مطالعه به توسعه یک سیستم جدید برای تعیین بافت خاک در محل مورد نظر، با استفاده از یک نفوذسنج مخروطی با سه زاویه مخروط 30، 45 و 60 درجه پرداخته شد. نفوذسنج مجهز به یک میکروفون بود که در آن صدای حاصل از اصطکاک مخروط-خاک برای تعیین بافت خاک استفاده شد. برای تشخیص سه نوع بافت خاک که شامل رس، شن و لوم بود، از روش آنالیز سیگنال‌های صوتی در حوزه زمان- فرکانس (تبدیل موجک) استفاده گردید. تجزیه سیگنال‌ها در 5 سطح انجام شد و ویژگی‌های مجموع (sum)، ریشه میانگین مربعات (rms)، واریانس var، کشیدگی (kurtosis) و ممان‌های مرتبه بالا moment4 مورد مقایسه قرار گرفتند. نتایج نشان داد که زیر سیگنال‌های جزئیات اول مخروط 30 و 60 درجه، جزئیات سوم مخروط 60 درجه، جزئیات چهارم مخروط 60 درجه و آپروکسیمت مخروط 45 و 60 درجه به‌ترتیب دارای بیشترین توانایی و اولویت برای تشخیص نوع بافت خاک از یکدیگر می‌باشند. در بین ویژگی‌های بررسی‌شده به‌ترتیب ویژگی‌های مجموع، واریانس، ممان، کشیدگی و ماکزیمم برای تشخیص نوع خاک با سطح احتمال 1% دارای اولویت هستند. مقادیر همه این ویژگی‌ها با افزایش اندازه ذرات خاک از رس تا شن افزایش یافت. تکنیک صوت دارای پتانسیل خوبی جهت تشخیص نوع بافت خاک بود.

feasibility of soil texture determination using acoustic signal processing of a cone penetrometer

introductionprecision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. precision agriculture offers the potential to automate and simplify the collection and analysis of information. it allows management decisions to be quickly made and implemented in small areas of larger fields. measuring acoustic signals with a cone penetrometer is an advanced and inexpensive method that provides a lot of information about the soil within the shortest amount of time and with the lowest cost. the texture of the soil determines the percentage of the constituents of the mineral part of the soil such as sand, silt, and clay.in this study, an acoustic penetrometer is developed to provide an accurate method for determining the soil texture. this system uses a microphone to record the sound produced by the cone-soil contact and correlates this data with the soil texture.materials and methodsan acoustic cone penetrometer (acpt) was designed to determine if there is a relationship between the sound produced at the cone-soil contact and soil particle size. three types of cones with angles of 30, 45, and 60 degrees, diameter of 20.27 mm, and rod length of 300 mm according to asae standard s313.3 feb1999ed (r2013) were used to determine the relationship between sound and soil texture and to choose the best angle. a microphone (20-20,000 hz) suitable for fast dynamic responses was used to record the audio signals produced from the soil. audio signals were stored online through the oscilloscope section of matlab software. to create the controlled vertical movement of the cones, a mechanical mechanism with electronic controllers was designed. this mechanism can be connected to the rails of the soilbin available in urmia university, iran, and is made of a 5 hp electric motor with a gearbox, an inverter for controlling the rotational speed of the electric motor, and a digital ruler for recording vertical movement. soil samples were tested in 19-liter bins.acoustic signals received from the microphone were processed in the time-frequency domain using wavelet transform. in this research, daubechi function type 3 is used to analyze acoustic signals. it is not possible to use the processed acoustic signals directly for statistical analysis. therefore, the relevant features should be extracted from them.  from the 30 features of time domain signals, the most effective and main features include: sum, max, rms, average, var, kurtosis, and moment4. they were ranked using the feature selection section of weka 3.9.2 software to avoid increasing the volume of calculations, increase processing speed, and reduce errors. the characteristic vector of the sub-signals of several different soil samples was analyzed to distinguish the soil type and constituents namely sand, silt, and clay.results and discussionthe best type of cone was selected using weka software. the number of features in the d1 sub-signals was higher for the 45-degree cone, and it can be concluded that with this cone, the soil type can be better recognized.the average values of characteristics in clay, loam, and sand had an increasing trend, respectively, and were statistically significant with a probability of 1% and 5%.acoustic signals for clay soil, which has a heavy texture and small particles, have minimum amplitude, and for loamy and sandy soils, they were observed as medium and maximum, respectively. this will cause the values of the selected features of clay soil to be low, and as a result, the average values, variance, and standard deviation are also low. they would be higher for loamy and sandy soil which have larger particles. it can be deduced that, as the size of the soil particles increases, the particles hitting the cone wall would become heavier and would affect the frequency and amplitude of the signal. this will result in the increase of signal amplitude values and, the sum, max, and mean values as well.conclusionamong the sub-signals, the maximum effect of soil texture type changes was related to d1 sub-signals for the 45̊ cone, and these signals had more potential to identify the soil texture type. among the features, the sum, average, var, and rms were significant at 1% probability levels. therefore, these features have more potential to detect the type of soil texture in the mentioned sub-signal. additionally, the effect of soil texture change on moment and kurtosis characteristics was significant at 5% probability levels.