Olive classification according to external damage using image analysis.

The external appearance of an olive’s skin is the most decisive factor in determining its quality as a fruit. This work tries to establish a hierarchical model based on the features extracted from images of olives reflecting their external defects. Seven commercial categories of olives, established by product experts, were used: undamaged olives, mussel-scale or ‘serpeta’, hail-damaged or ‘granizo’, mill or ‘rehús’, wrinkled olive or ‘agostado’, purple olive and undefined-damage or ‘molestado’. The original images were processed using segmentation, colour parameters and morphological features of the defects and the whole fruits. The application of three consecutive discriminant analyses resulted in the correct classification of 97% and 75% of olives during calibration and validation, respectively. However the correct classification percentages vary greatly depending on the categories, ranging 80–100% during calibration and 38– 100% during validation

Discrimination of sunflower, weed and soil by artificial neural networks

Selective application of herbicide to weeds at an early stage in crop growth is an important aspect of site-specific management of field crops, both economically and environmentally. This paper describes the application of a neural network classifier to differentiate between 2 and 3 weeks old sunflower plants and common cocklebur weeds of similar size, shape and colour. Colour images were obtained by a digital camera, in natural sunlight. A specific objective was to minimise the subsequent image processing operations needed to enhance the images and to extract the features needed by a back propagation neural network classifier. Neural network structures with different numbers of hidden layers and neurons in them were tested to find the optimal classifier. The maximum number of correctly recognised images in distinguishing weeds from sunflower plants was 71 (out of 86), while it was 82 and 74 in separating sunflower and weed images from bare soil images, respectively. (C) 2004 Elsevier B.V. All rights reserved

Apple sorting using artificial neural networks and spectral imaging

Empire and Golden Delicious apples were sorted based on their surface quality conditions using backpropagation neural networks. Pixel gray values and texture features obtained from the entire apple image were used as input to artificial neural network classifiers. Two classification applications were performed: a 2-class classification that included a defective (or stem/calyx) apple group and a good apple group, and a 5-class classification that included all the defective and good apple groups. Effective image resolution was evaluated to shorten the training and testing times in classification with neural networks. Resolution size of 60 x 80 pixels was identified to be efficient and used in all of the classification applications. Effective spectral bands for identification of specific surface characteristics were determined in the 2-class and 5-class classification applications. Artificial neural network classifiers successfully separated apples with defects from non-defective apples without confusing the stem/calyx with defects. Classification success in the 2-class classification ranged from 89.2% to 100%. In the. 5-class classification, classification success for Empire apples was between 93.8% and 100%, while classification success for Golden Delicious apples was between 89.7% and 94.9% based on the features used

Soil aggregate sequestration of cover crop root and shoot-derived nitrogen

Cover crop roots and shoots release carbon (C) and nitrogen (N) compounds in situ during their decomposition. Depending upon the season, these C and N compounds may be sequestered, the C may be respired or the N may be leached below the root zone. A field study was established to identify the contributions of cover crop root and shoot N to different regions within aggregates in the A(P) horizon of a Kalamazoo loam soil. Fall-planted rye plants (Secale cereale L.) were labeled the next May with foliar applications of solutions containing 99% atom ((NH4)-N-15)(2)SO4. Isotopic enrichment of soil aggregates ranging from 2.0 to 4.0, 4.0-6.3 and 6.3-9.5 mm across was determined following plant residue applications. Concentric layers of aggregates were removed from each aggregate by newly designed meso soil aggregate erosion (SAE) chambers. Non-uniform distributions of total N and recently derived rye N in soil macroaggregates, across time, suggested that the formations and functions of macroaggregates are very dynamics processes and soil aggregates influence where N is deposited. Early in the season, more 15 N migrated to the interior regions of the smallest aggregates, 2-4 mm across, but it was limited to only surfaces and transitional regions of the larger aggregates, 6.3-9.3 mm across. Exterior layers of aggregates between 6.0 and 9.5 mm retained 1.6% of the N-derived from roots in July 1999, which was three times more than their interior regions. This was slightly greater than the % N-derived (from shoot). One month later, as the maize root absorption of N increased rapidly, % Nderived front roots and % Nderived from shoot were nearly equal in exterior layers and interior regions of soil aggregates. This equilibrium distribution may have been from either greater diffusion of N within the aggregates and/or maize root removal form aggregate exteriors. Results supported that most of roots grew preferentially around surfaces of soil aggregates rather than through aggregates. Cover crop roots contributed as much N as cover crop shoots to the total soil N pool. Subsequent crops use N from the most easily accessible zones of soil structure, which are surfaces of larger soil aggregates. Therefore maintaining active plant roots and aggregated soil structure in the soil enhances N sequestration and maximize soil N availability. These studies suggest that the rapid and perhaps bulk flow of soil N solutions may bypass many of the central regions of soil aggregates, resulting in greater leaching losses

GIS monitoring and evaluation of nitrogen pollution in the waters of Troy, Turkey

Troy in Turkey is not only important from an archeological point of view, but also from an ecological perspective as well. The waters of Troy have been used as drinking source for the birds and other animals and are still used as an irrigation source by farmers. This study was carried out to monitor and determine the amounts of NO3-N, NH3-N and NO2-N ions and their spatial and temporal changes from December 2002 to September 2003 in the Troy water resources. Water samples were collected from 25 sampling points at each sampling time. Samples were preserved according to specific test requirements and immediately analyzed for NO3-N, NH3-N, and NO2-N ions using a LaMotte smart colorimeter. Global positioning system (GPS) was used to determine the coordinates of the sampling points. Producing maps and statistical analyses results indicate that both ammonia and nitrate nitrogen concentrations started to increase after May in all water resources. These changes were attributed to land use types and crop growing periods in the area. Nitrate concentration ranged from 0 to 45 mg L -1, ammonia from 0 to 118 mg L-1 and nitrite from 0 to 3.5 mg L-1. One well, two drainage canals, and two river samples have clearly elevated nitrate levels without elevated chloride, and this suggests fertilizers as source of nitrate in water. There are positive and significant linear relationships between nitrate and chloride concentrations in two wells suggesting that the water is being impacted by domestic sewage. © by PSP

Impact of land cover types on soil aggregate stability and erodibility

Gok double dagger eada is the biggest island, and it is also known as the organic island of Turkey. Approximately 65% of the Gok double dagger eada lands have slope > 12%. Climate, topography, land cover, and soil characteristics are considered to be the main natural factors affecting soil erosion severity in the Gok double dagger eada. Prevention of soil degradation, hence the preservation or improvement of the overall quality of the soil, is directly related to the presence of stable soil aggregates. In addition, the resistance to weathering and replacement of soil particles are also relevant aspects in terms of sustainability. Aggregate stability (AS) and erodibility of land (Kfac) are related to soil properties. However, this relationship can vary under different circumstances. In this study, 248 surface soil samples have been taken from forest and semi-natural areas (FSNA) and agricultural areas (AGRA) according to CORINE 2006. Eleven selected soil properties were measured, and their impacts on AS and Kfac (RUSLE-K) were determined by using the CRT (classification and regression tree) in Gok double dagger eada. Results showed that the relations among soil characteristics changed according to the land cover classes. Total organic carbon is much more associated with AS in AGRA, while total carbon is associated with AS in FSNA. The effect of calcium carbonate on Kfac was higher than other soil properties when the land cover type was ignored. On the other hand, in AGRA, the effect of between clay content on Kfac was greater than those of FSNA.COMU-BAP Project [2012/17]This study was supported by the COMU-BAP Project 2012/17

Changes in soil quality parameters after a wildfire in Gelibolu (Gallipoli) National Park, Turkey

The objective of this study was to determine the influence of forest wildfires that occurred 1994 in Gelibolu (Gallipoli) National Park, Turkey, on some physical, chemical and biological properties of soils. Soil samples were collected from five different locations as replicates from both burned and nearby unburned sites. Results showed that available phosphorus and potassium content, pH, and electrical conductivity of burned soils were higher than those of unburned counterparts. On the other hand, aggregate stability, hydraulic conductivity, total porosity, soil water content, cation exchange capacity, total nitrogen, urease activity, and microbial biomass carbon values of burned soils were lower than those of unburned ones. The mean soil organic carbon values were 2.94% for burned and 5.01% for unburned soils, whereas those of microbial biomass were 1.2 mg C g soil(-1) and 1.69 mg C g soil(-1). Aggregate stability values were found to be 88.32% and 94.44% (P < 0.05), and urease activities were 185 mg kg(-1) 2-h(-1) and 366 mg kg(-1) 2-h(-1) (P < 0.01) for burned and unburned soils samples, respectively. This research showed that negative effects of fire still remain in the soil even after 8 years and recovery of soil health was very low

Apple Sorting Using Artificial Neural Networks and Spectral Imaging

Empire and Golden Delicious apples were sorted based on their surface quality conditions using backpropagation neural networks. Pixel gray values and texture features obtained from the entire apple image were used as input to artificial neural network classifiers. Two classification applications were performed: a 2-class classification that included a defective (or stem/calyx) apple group and a good apple group, and a 5-class classification that included all the defective and good apple groups. Effective image resolution was evaluated to shorten the training and testing times in classification with neural networks. Resolution size of 60 x 80 pixels was identified to be efficient and used in all of the classification applications. Effective spectral bands for identification of specific surface characteristics were determined in the 2-class and 5-class classification applications. Artificial neural network classifiers successfully separated apples with defects from non-defective apples without confusing the stem/calyx with defects. Classification success in the 2-class classification ranged from 89.2% to 100%. In the 5-class classification, classification success for Empire apples was between 93.8% and 100%, while classification success for Golden Delicious apples was between 89.7% and 94.9% based on the features used

Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features

Empire and Golden Delicious apples were classified based on their surface quality conditions using backpropagation neural networks (BPNN) and statistical classifiers such as decision tree (DT), K nearest neighbour (K-NN) and Bayesian with textural features (and histogram features only with the BPNN classifier) extracted using all the pixels in an entire apple image. Two classification applications were performed: two subsets that included a defective (or stem/calyx) apple group and a good apple group; and five subsets that included all the defective (leaf roller, bruise and puncture on Empire, and bruise bitter pit and russet on Golden Delicious) and good apple groups (good tissue and stem/calyx views). With two subsets, classification accuracy using textural features ranged between 72(.)2 and 100% for Empire apples while it ranged between 76(.)5 and 100% for Golden Delicious apples. Results obtained using histogram features were significantly lower than the other classification applications. With five subsets, slightly lower recognition accuracies were obtained; the BPNN using textural features performed 93(.)8% success rate in recognising Empire apples. However, for Golden Delicious apples, all the classifiers produced similar accuracy rates ranging between 85(.)9 and 89(.)7%. Results obtained from the BPNN using histogram features were significantly lower than the classification applications using textural features. (C) 2004 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd

Gis monitoring and evaluation of nitrogen pollution in the waters of Troy, Turkey

Troy in Turkey is not only important from an archeological point of view, but also from an ecological perspective as well. The waters of Troy have been used as drinking source for the birds and other animals and are still used as an irrigation source by farmers. This study was carried out to monitor and determine the amounts of NO3-N, NH3-N and NO2-N ions and their spatial and temporal changes from December 2002 to September 2003 in the Troy water resources. Water samples were collected from 25 sampling points at each sampling time. Samples were preserved according to specific test requirements and immediately analyzed for NO3-N, NH3-N, and NO2-N ions using a LaMotte smart colorimeter. Global positioning system (GPS) was used to determine the coordinates of the sampling points. Producing maps and statistical analyses' results indicate that both ammonia and nitrate nitrogen concentrations started to increase after May in all water resources. These changes were attributed to land use types and crop growing periods in the area. Nitrate concentration ranged from 0 to 45 mg L-1, ammonia from 0 to 118 mg L-1 and nitrite from 0 to 3.5 mg L-1. One well, two drainage canals, and two river samples have.clearly elevated nitrate levels without elevated chloride, and this suggests fertilizers as source of nitrate in water. There are positive and significant linear relationships between nitrate and chloride concentrations in two wells suggesting that the water is being impacted by domestic sewage