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Reference evapotranspiration (ET0) is an important driver in managing scarce water resources and making decisions on real-time and future irrigation scheduling. Therefore, accurate prediction of ET0 is crucial in water resources management. In this study, the prediction of ET0 was performed employing several optimization algorithms tuned Fuzzy Inference System (FIS) and Fuzzy Tree (FT) models, for the first time, whose generalization capability was tested using data from other stations. The FISs and FTs were developed through parameter tuning using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Pattern Search (PS), and their combinations. The FT was developed by combining several fuzzy objects that received ranked meteorological variables. A total of 50 FIS and FT models were developed and the model ranking was performed utilizing Shannon’s Entropy (SE). Evaluation outcomes revealed the superiority of the hybrid PSO-GA tuned Sugeno type 1 FT model (with R = 0.929, NRMSE = 0.169, accuracy = 0.999. NS = 0.856, and IOA = 0.985) over others. For evaluating the generalization capability of the models, three different parts of datasets (all-inclusive, 1st half, and 2nd half) of the five test stations were evaluated. The proposed model performed similarly well, according to the findings, on the datasets of the test stations and that the proposed model developed using Sugeno type 1 FT provided superior performance. The study concluded that the proposed optimization algorithm tuned Sugeno type 1 FT approach which was composed of several standalone FIS objects was suitable for predicting daily ET0 values.

Classification of crop species is an actively studied topic in remote sensing using multispectral image sensors. Unfortunately, the spectral bands available in the multispectral imagery are broad and limited in number to classify the crop species. In this paper, we propose optimal spectral bands to classify Allium (garlic and onion) and Brassicaceae (Chinese cabbage and radish) by using higher-dimensional data from hyperspectral imagery. A decision-tree classifier was used to determine the optimal method to use the high-dimensional data. The high-dimensional data were analysed for all growth stages and considering bandwidths with different full width at half maximum (FWHM) values at 25, 40, 50 and 80 nm. The spectral bands selected for Allium were differentiated into green, blue, and NIR bands for each growth stage. The results show that Allium can be classified clearly as overall accuracy (OA) 1 and kappa coefficient 1 for all FWHM based on March 22 data. For each April 19 and May 12 data, the decision-tree classifier with each 80 nm FWHM and 50 nm FWHM yielded a better classification accuracy of more than OA 0.921 and kappa coefficient 0.839 than other FWHM. The spectral bands selected for Brassicaceae were found to be similar to blue band for all growth stages. Brassicaceae was classified clearly for all FWHM based on October 27 data. Also, Brassicaceae was classified clearly for 25 nm FWHM based on November 25 data and OA, kappa coefficient for 40 nm FWHM and 50 nm FWHM are high as 0.974, 0.947 respectively.

Recently, remote sensing technology as a nondestructive method has been utilized to detectthe quantity and quality of crops using unmanned aerial system. To predict vegetation growth(leaf dry mass and nitrogen content) of soybean, two vegetation index(NDVI and Green NDVI)were calculated from images acquired by multi-spectral camera mounted on a UAV and eachprediction models between vegetation growth and index were evaluated. As a result, there wasno significant difference between vegetation growth and index when each vegetation stage foreach yellow and black bean were compared to each other. However, there was significantdifference between vegetation growth and index when all vegetation stage for each yellow andblack bean were compared to each other. Moreover, there was significant difference betweenvegetation growth and NDVI(r= 0.799 for leaf dry mass, r= 0.796 for nitrogen content), andGreen NDVI(r= 0.860 for leaf dry mass, r= 0.845 for nitrogen content) for all vegetation stageswith all soybeans. The accuracy and precision of Green NDVI model(R2= 0.740 for leaf drymass, R2= 0.714 for nitrogen content) were better than those of NDVI model regardless ofvarieties and vegetation growth. Therefore, Green NDVI has considerable potential to detect thequantity and quality of soybeans.