فهرست مطالب حسنا محمدی منور

Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for early detection of FB via spectrometry method.

Recently, optical GreenSeeker sensor, because of its accuracy and user friendly, has attracted the attention of many farmers to use it for measuring Normalized Difference Vegetation Index (NDVI). The main objective of this study was to measure the total leaf area of plants using this optical sensor and investigate the possibility of using this sensor instead of conventional methods. The images of all plots were recorded using camera and the G-R (Green-Red) was determined as a best function to distinguish soil from the leaf. Based on the Results, an appropriate relationship between total leaf area and NDVI was obtained (R2=0.84). Results of comparing Greenseeker and chlorophyll meter SPAD-502 indicated a promising accuracy of the Greenseeker sensor (R2=0.82) in measuring the plant chlorophyll. The root mean square error resulted from the effect of organic matter on sensor output was found to be 0.048. It must be noted that every product has a specific reflection in the red and infrared bandwidth, so the model obtained in this study just can be used for corn. For the other crops it is necessary to develop specific models. Finally, it is concluded that the GreenSekeer sensor can be used to determine the total top area of canopy and it provides the possibility of comparing the total leaf area of canopy between fields.

Precision agriculture (PA) is a technology that measures and manages within-field variability, such as physical and chemical properties of soil. The nondestructive and rapid VIS-NIR technology detected a significant correlation between reflectance spectra and the physical and chemical properties of soil. On the other hand, quantitatively predict of soil factors such as nitrogen, carbon, cation exchange capacity and the amount of clay in precision farming is very important. The emphasis of this paper is comparing different techniques of choosing calibration samples such as randomly selected method, chemical data and also based on PCA. Since increasing the number of samples is usually time-consuming and costly, then in this study, the best sampling way -in available methods- was predicted for calibration models. In addition, the effect of sample size on the accuracy of the calibration and validation models was analyzed.
Two hundred and ten soil samples were collected from cultivated farm located in Avarzaman in Hamedan province, Iran. The crop rotation was mostly potato and wheat. Samples were collected from a depth of 20 cm above ground and passed through a 2 mm sieve and air dried at room temperature. Chemical analysis was performed in the soil science laboratory, faculty of agriculture engineering, Bu-ali Sina University, Hamadan, Iran. Two Spectrometer (AvaSpec-ULS 2048- UV-VIS) and (FT-NIR100N) were used to measure the spectral bands which cover the UV-Vis and NIR region (220-2200 nm). Each soil sample was uniformly tiled in a petri dish and was scanned 20 times. Then the pre-processing methods of multivariate scatter correction (MSC) and base line correction (BC) were applied on the raw signals using Unscrambler software. The samples were divided into two groups: one group for calibration 105 and the second group was used for validation. Each time, 15 samples were selected randomly and tested the accuracy of models, then other15 samples were added randomly to the previous set and it was done continuously. Finally, seven groups (15, 30... 105) were placed in each category.
All regression models on the whole pre-processed soil spectra were obtained in absorption mode. By increasing the number of samples in the calibration set of random group, RMSE was decreased from 0.2 to 0.13 nonlinearly. RMSE in the chemical test was also decreased almost linearly from 0.17 to 0.11. At the same time, R2 and RPD were increased from 0.46 to 0.72 and from 1.3 to 2.0 respectively. Finally, in categories based on PCA, the RMSE fell down almost linearly (0.19-0.13). Potassium prediction model with the least amount of R2 (0.48) and phosphorus with highest number of errors (RMSE = 5.28) were the weakest models in whole data. Other properties of the soil had a higher coefficient of determination (R2> 0.5). Therefore, prediction models had acceptable accuracy. At least 77, 105 and 105 samples are required for precise calibration model of OC, nitrogen and pH respectively. Due to the different conditions of farms, comparing these results with previous findings is very complex. Furthermore, model accuracy did not improve by increasing data of calibration models to the total number of samples. While in previous studies, more precise model was calibrated by considering the entire data sets. Among all factors of soil, acidity has little dependence on the other soil properties. The pH modeling is also confirmed by Moros (2009) however, the more error was reported here. There is no certain pH range in the NIR spectra, and usually it is distinguishable from the other properties of the soil (Kuang and Mouazen, 2011).
Spectroscopic methods exhibited good potential for detecting soil properties. MSC and BC can effectively remove irrelevant information to improve prediction accuracy. Using different methods to select numbers of data for the calibration models presented similar results, but in the meantime PCA technique provided the best answer. Supplementary, the ever-increasing number of data does not always improve modeling accuracy. It is better to choose numbers of data according to principal components (PCs) of PCA to obtain acceptable answer. It must be noted that every crops requires a specific soil and nutrients, so it is necessary to develop models for other soil properties.

One of the most important ways to increase sales and marketability of the agricultural products is the classifying them according to the morphological characteristics and the internal quality. Water content and soluble solid content are two important quality factors of carrot. Although other ways such as image processing can be used for classifying, the spectroscopic method can be a better choice for measuring the internal quality than others. In this research optical method based on near infrared spectroscopy (900-1600 nm) has been used to determine moisture content and brix index in carrot. A total of 60 samples were used for the modeling, whereas 40 samples were used for the calibration set and 20 samples were used for prediction set. Four pre-processing methods, including average smoothing, multiplicative scatter correction (MSC), first and second derivatives, were applied to improve the predictive ability of the models. Then models were developed by partial least squares (PLS). The correlation coefficient (R2) and root mean square error of prediction (RMSEP) were 0.92 and 1.13 for moisture content, whereas 0.96 and 1.07 for SSC., respectively. The results show that NIR can be used as a rapid method to determine soluble solid content and moisture content in carrot.

Sensors and new technologies such as wireless sensor networks have developed significantly in the industrial sector and recently have been caused dramatic changes in the precision farming area. Using these technologies and instruments¡ more information will be gathered from field condition and it is possible to control inputs¡ efficiently. Considering wireless sensor network capability in data collection and controlling of the actuators¡ this technology has attracted many customers in the domestic¡ industrial and military sectors. But due to lack of sufficient information on sensors and new technologies¡ they have not been used in Iran’s agriculture. So this paper tries to introduce sensors and modern technologies that have been used in agriculture and also to investigate advantages and limitations of the WSN in agriculture.