فهرست مطالب h. mohamadi-monavar

Remote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall which has a significant effect on the spectral characteristics of plants. In this study, Landsat 8 and MODIS satellite images were used to identify and separate two irrigated and rain-fed wheat farms in Hamadan province. Two algorithms of support vector machine (SVM) and minimum distance (MD) were used simultaneously to classify irrigated and rain-fed farms. In the next step, the area under cultivation of rain-fed and irrigated wheat was predicted in the whole cultivated area of Hamadan province. Finally, the cultivation area of rain-fed and irrigated crops was calculated in the province using Sentinel 3 satellite images based on the random forest algorithm in 2016.

The study area is Hamedan province, which is located between 59◦ 33′ and 49◦ 35′ north latitude and also from 34◦ 47′ to 34◦ 49′ east longitude of the Greenwich meridian. A 50-hectare rain-fed wheat farm in Amzajerd was used as a sample to extract the properties of rain-fed wheat. Also, irrigated indices were extracted from a 100-hectare irrigated wheat farm located in Kaboudrahang. Satellite images were applied to separate irrigated and rain-fed wheat in Hamadan province. NDVI, EVI and NDWI indices were extracted from 16-day images of Landsat, MODIS, and Sentinel 3 sensors in the five-year period (2015-2019). Google Earth Engine (GEE) system was the environment for performing image processing calculations and extracting indices and maps.

The NDVI and EVI of irrigated and rain-fed wheat farms were calculated in 2015-2019. A small peak was observed in the rain-fed and irrigated NDVI trend in November due to the early germination of wheat leaves in winter, and the larger peak in May and June showed the maximum greenness of irrigated and rain-fed wheat, respectively. The ascending or descending trend of NDVI / EVI had no constant slope. This can be due to changes in meteorological parameters, which sometimes cause a sudden increase or decrease in the values of these indices. Despite the non-linearity of the NDVI / EVI trend over time, the maximum greenness was recorded just a month before the wheat harvest, which was seen in the third decade of May to the first decade of June. One of the cases is the sharp drop of NDVI / EVI after its final peak, which was definitely due to yellowing wheat and harvesting. Since the distinction between rain-fed and irrigated crops was difficult only based on NDVI, NDWI was also used to determine the water content of wheat so that irrigated wheat could be identified. However, the difference between rain-fed and irrigated wheat in terms of NDWI spectral density was insignificant; the maximum and minimum occurrence times of NDWI and NDVI of rain-fed and irrigated wheat were chosen for their separation. In order to map the cultivation area, in addition to the MODIS sensor, Sentinel 3 was used due to its ability to detect chlorophyll accurately. Due to the fact that the imaging of the Sentinel 3 satellite started since 2016, the map of rain-fed and irrigated cultivation as well as the cultivation area and their separation was done based on the random forest algorithm in 2016.

The results of this study showed that the appropriate method for distinguishing between rain-fed and irrigated wheat is the simultaneous use of several indices. Also, the greatest difference is in the maximum greenness, which happened almost one month before harvest. MD and SVM classification algorithms could distinguish irrigated and rain-fed wheat from other crops with 90% and 80% accuracy, respectively. Distinguished maps of irrigated and rain-fed crops based on the random forest algorithm were obtained using Sentinel 3 satellite imagery which can show the fertility of agricultural lands in the province.

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.

One of the most important factors in agricultural production is nitrogen which has a great impact on plant growing, yield performance and plant quality production. The optimum amount of nitrogen fertilizer is varied from fields to fields. There are some time consuming and costly ways to measure the nitrogen content of plants or soil, which are inappropriate for extended field or for a long growing season. Fast and remote optical sensors calculate greenness of plant using reflectance or absorbance of light from green leaves. Measuring chlorophyll with SPAD managed the nitrogen requirement for maize, Poinsettia and Nagoya Red. Whereas SPAD was not a suitable choice for chlorophyll measurement at the end of growing period. Therefore, GreenSeeker was applied as a non-contact to record the NDVI of tomato’s and cucumber’s leaves. The purpose of this research was the evaluation of GreenSeeker ability to estimate nitrogen requirement and then the plant health. The study was performed on Matin and Nahid cultivars of tomato and cucumber, respectively. The pots were 291 and filled with 3 kg sieved soil. The bottom layer of each pot was filled with stone for better drainage. Before planting, the soil was analyzed in order to define the ingredients. All pots put in the greenhouse with polycarbonate structure in two floors. Measurements were repeated every week with SPAD and GreensSeeker and fertigation was started 50 days after planting (DAP). In order to provide other nutrient elements, all pots got Humic-acid at 37DAP and the effect was measured in 43rd DAP. Fertigation was continued until 71st DAP and first, second and third treatments were supplemented with extra fertilizer to reach the amount of fertilizer to fifth treatment. To calculate Total Nitrogen (TN), the concentrations of nitrate-N and nitrite-N are determined and added to the total Kjeldahl nitrogen. Chlorophyll meter (SPAD) and GreenSeeker optical sensor have become available for site-specific and need-based N management in greenhouse. The GS was located at 60 cm above the plant and measured the average NDVI. This sensor has red and NIR diodes which reflect and absorb the spectra in 660±15nm and 770±15nm regions, respectively. The SPAD values were recorded by inserting the middle portion of the index leaf in the slit of SPAD meter. As well as, chlorophyll meter can confirm the GreenSeeker output (NDVI). GreenSeeker is a suitable optical sensor because it is not affected by light and temperature variation or wind intensity.
Statistical analyses were performed on the pooled data of both tomato and cucumber using Statistical Product and Service Solutions (SPSS). Regression equations were fitted between fertilizer and the readings recorded with different gadgets at different growth stages. Chlorophyll content and NDVI of tomato and cucumber increased during the growing stages except in 71st DAP for cucumber. The percentage of total nitrogen of 1st, 2nd and 3rd treatments were further than two others because of supplementary fertilizer. According to the Kjeldahl result of cucumber, the 3rd treatment had the lowest nitrogen accumulation in fruits. In addition, chlorophyll and NDVI of cucumber almost showed the increasing correlation by fertilizer enhancement while the opposite behavior was seen for tomato. That would be related to different fertilizer needs of them. The linear regression of fertilizer and reading NDVI of 2nd to 5th treatments were ascending. The number of increasing leaves was calculated in all pots every weeks as another studied element. Each pot had new grown leaves every weeks that was more or sometimes less than last weeks. However, accurate correlation coefficient was reported with NDVI in all treatments, whereas chlorophyll did not show a direct relation. The result of the study confirmed the useful GreanSeeker as an accurate and fast technology for prediction of NDVI. Among different fertilizer treatments of cucumber, 3rd one showed the acceptable results. Since tomatoes did not reach to fertility stage, it would not possible to extract the best nitrogen fertilizer treatments. It is obvious that evaluation of pots in complete growth stages reach us to codify manual fertilization.