An automatic visible-range video weed detection, segmentation and classification prototype in potato field

Weeds might be defined as destructive plants that grow and compete with agricultural crops in order to achieve water and nutrients. Uniform spray of herbicides is nowadays a common cause in crops poisoning, environment pollution and high cost of herbicide consumption. Site-specific spraying is a possible solution for the problems that occur with uniform spray in fields. For this reason, a machine vision prototype is proposed in this study based on video processing and meta-heuristic classifiers for online identification and classification of Marfona potato plant (Solanum tuberosum) and 4299 samples from five weed plant varieties: Malva neglecta (mallow), Portulaca oleracea (purslane), Chenopodium album L (lamb's quarters), Secale cereale L (rye) and Xanthium strumarium (coklebur). In order to properly train the machine vision system, various videos taken from two Marfona potato fields within a surface of six hectares are used. After extraction of texture features based on the gray level co-occurrence matrix (GLCM), color features, spectral descriptors of texture, moment invariants and shape features, six effective discriminant features were selected: the standard deviation of saturation (S) component in HSV color space, difference of first and seventh moment invariants, mean value of hue component (H) in HSI color space, area to length ratio, average blue-difference chrominance (Cb) component in YCbCr color space and standard deviation of in-phase (I) component in YIQ color space. Classification results show a high accuracy of 98% correct classification rate (CCR) over the test set, being able to properly identify potato plant from previously mentioned five different weed varieties. Finally, the machine vision prototype was tested in field under real conditions and was able to properly detect, segment and classify weed from potato plant at a speed of up to 0.15 m/s.This work was supported in part by MINECO under grant number RTI2018-098958-B-I00, Spain, and by the European Union (EU) under Erasmus+ project entitled Fostering Internationalization in Agricultural Engineering in Iran and Russia [FARmER] with grant number 585596-EPP-1-2017-DE-EPPKA2-CBHE-JP

Design, manufacture and evaluation of the new Instrument to Measure the ‎Friction Coefficient of Soil

Accurate determination of soil parameters such as the coefficient of internal friction, soil adhesion and soil-metal friction is essential for designing agricultural machinery, calculating the draft force and investigating the performance and wear of them. Tillage as the main operation is causing soil displacement and skidding on tillage equipment. Soil friction parameter against the tools that have wide contact surface with soil, increases the operating draft force and consequently energy consumption would be increased. This paper describes the design, fabrication and using a system for measuring the coefficient of soil external friction. The result showed that the changes of draft force versus normal load were linear and increasing the moisture increased soil external friction. Also, the results showed that the test system can discriminate between different soil textures and different contact surfaces tested. In general, according to the results the performance of the soil friction coefficient measuring device was acceptable

A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Application of Computational Intelligence Methods for Predicting Soil Strength

The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (R2) were calculated at 2.54% and 0.979, respectively

Effect of Pretreatments on Convective and Infrared Drying Kinetics, Energy Consumption and Quality of Terebinth

This study is focused on the influence of convective drying (50, 60, and 70 °C) and infrared (IR) power (250, 500, and 750 W) on the drying kinetics, the specific energy consumption of terebinth drying as well as quality and bioactive compounds upon various pretreatments such as ultrasound (US), blanching (BL), and microwave (MW). Compared to convective drying, IR drying decreased more the drying time and energy consumption (SEC). Application of higher IR powers and air temperatures accelerated the drying process at lower energy consumption (SEC) and higher energy efficiency and moisture diffusion. Terebinth dried by a convective dryer at 60 °C with US pretreatment showed a better color compared to other samples. It also exhibited the polyphenol and flavonoid content of 145.35 mg GAE/g d.m. and 49.24 mg QE/g d.m., respectively, with color variations of 14.25 and a rehydration rate of 3.17. The proposed pretreatment methods significantly reduced the drying time and energy consumption, and from the other side it increased energy efficiency, bioactive compounds, and quality of the dried samples (p < 0.01). Among the different pretreatments used, microwave pretreatment led to the best results in terms of the drying time and SEC, and energy efficiency. US pretreatment showed the best results in terms of preserving the bioactive compounds and the general appearance of the terebinth

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively

Predicting soil fragmentation during tillage operation using fuzzy logic approach

Abstract One of the main characteristics of the soil structure, which affects the plant growth and its yield, is its aggregates size. Correct tillage operations leads to prevention from soil degradation and help to maintain and improve its physical, chemical, and biological characteristics. In this paper, a model based on fuzzy logic approach was used to describe the soil fragmentation for seedbed preparation in the composition of primary and secondary tillage implements of subsoiler, moldboard plow and disk harrow as conventional tillage composition in the region. Field experiments were carried out at educational and research farms of faculty of agriculture, University of Mohaghegh Ardabili. In this paper, an intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict soil fragmentation during tillage operation. The model inputs included soil moisture content, tractor forward speed and soil sampling depth. The fuzzy model consisted of 50 rules, in which three parameters of root mean square error (RMSE), relative error (e), and coefficient of determination (R 2 ) were used to evaluate the fuzzy model. These parameters were calculated 0.167%, 3.95%, and 0.988%, respectively. According to the results of this research, the fuzzy model can be introduced as one of the methods for predicting soil fragmentation during the tillage operation with high accuracy

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

This study aimed to predict the drying kinetics, energy utilization (Eu), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (Deff), Eu, EUR, exergy efficiency, and exergy loss. The value of the Deff was varied from 4.19 × 10–10 to 1.18 × 10–9 m2/s. The highest value Eu, EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R2) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict Eu, EUR, exergy efficiency, and exergy loss, with R2 of .9989, .9988, .9986, and .9978, respectively

Investigation of the Effect of Soil Moisture Content, Contact Surface Material and Soil Texture on Soil Friction and Soil Adhesion Coefficients

Soil friction and soil adhesion increase the implement draft force and energy consumption particularly in the tools that have larger contact area with soil. The main ways of lowering the total draft force of the tillage tools include the use of proper materials in tools structures as well as application of the tools in appropriate soil moisture content condition. This paper investigates the effects of soil moisture content, contact surface material and soil texture on soil friction and soil adhesion coefficients. To measure the coefficients of soil friction and soil adhesion, a measurement system was developed at the University of Mohaghegh Ardabili. Experiments for each soil texture were performed at five levels of soil moisture content and four contact materials of steel, cast iron, rubber, and teflon with three replications. Results have shown that in all soil types, the effects of soil moisture content and contact materials had a significant effect on the coefficient of both soil friction and soil adhesion at the probability level of 1%. The coefficient of friction increased with soil moisture content increment and reached its maximum and then had a drop in the fluid phase. Results have shown that the mean values of soil friction and soil adhesion coefficients were significantly different from the studied soils