Precision Farming
J. Nasrollahi Azar; R. Farrokhi Teimourlou; V. Rostampour
Abstract
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 ...
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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.
F. Jannatdost; P. Ahmadi Moghaddam; F. Sharifian
Abstract
Introduction Fruits and vegetables play an important role in food supply and public health. This group of agricultural products due to high humidity are perishable and most of them (5 to 50 percent) waste during post-harvest operation. Decreasing and minimizing such waste as "hidden harvest" could be ...
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Introduction Fruits and vegetables play an important role in food supply and public health. This group of agricultural products due to high humidity are perishable and most of them (5 to 50 percent) waste during post-harvest operation. Decreasing and minimizing such waste as "hidden harvest" could be an effective way to save food and increase profitability. Despite the surplus of the fruit production in the country, our position in terms of exportation is not commensurate with production, so measurements and grading on the basis of qualitative parameters such as firmness, taste, color, and shape can influence the marketing and export of fruit. In this research, application of an acoustic test is considered to achieve an effective and economic technology in the field to determine the stiffness of kiwifruit in post-harvest step. The aim of this study is to investigate the stiffness index of kiwifruit and provide a classification algorithm in the post-harvest step by using the non-destructive method of processing impact acoustic signals. Materials and Method In this research, an acoustic-based intelligent system was developed and the possibility of using the acoustic response to classify kiwifruit into soft, semi-soft and stiff categories was studied. 150 samples of Hayward variety of Kiwifruit was used during the 18 days shelf life in controlled conditions of temperature and humidity. Analyses were done in 9 sets per two days. In each analysis, an acoustic test was done by 48 samples in both free fall condition and fall from a conveyor belt. The feature extraction of acoustic signals in both the time domain and frequency domain has done, then the classification of samples was done by using the Artificial Neural Network. After getting the impact signals of stiff, semi-soft and soft samples, stiffness of kiwifruits identification has done by using acoustic features. The stiffness of kiwifruit samples in this study was measured to be 15.9±4.9 (N) by using the Magnes- Taylor test. Finally, samples were classified into stiff, semi-soft and soft by comparison of maximum force and flux of signals amplitude. Results and Discussion The results showed that the features of CF and maximum amplitude in the time domain have high accuracy in kiwifruit classification. The frequency resonances as environmental noises or impact position are out of control in the time domain which causes a decrease in accuracy. So, the ANN by features of time domain has not the acceptable capability to identify the semi-soft samples. The identification of semi-soft samples is not easy because of having same properties of stiff and soft samples. Extracted features of frequency domain have the most capability of correct detection. The optimal network has five neurons in the hidden layer and 0.014782 of mean square error. The accuracy of correct detection of the optimal network was 93.3, 91.3 and 78.3 percent for stiff, semi-soft and soft samples, respectively. Because of using more features in the frequency domain, the classification of all categories was acceptable and identification of semi-soft samples was as good as stiff and soft samples. The results of combined features of time and frequency domain showed that the artificial neural network has less efficiency in comparison with the other two attitudes. The accuracy of identification and classification was decreased by adding the extracted features of the time domain. So achieving the most accuracy in classification is accomplishable just by using the features of the frequency domain. By comparing the results of both free fall and online tests, it is claimed that this research can be industrialized. Conclusion Comparison of all results shows that there was no significant difference in the capability of ANN for identification and classification of the sample in three categories. After all, we can use this method in online sorting of kiwifruits by controlling the vector and position of impaction.
