GIS&RS
sayyad asghari; hamid Soleimani Youzband; Aboozar Sadeghi
Abstract
Cereals are considered one of the most important sources of dietary protein, and wheat is a significant cereal crop with high protein content. Currently, the rapid and excessive population growth and the perceived shortage of available resources to meet essential human needs are among the biggest challenges ...
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Cereals are considered one of the most important sources of dietary protein, and wheat is a significant cereal crop with high protein content. Currently, the rapid and excessive population growth and the perceived shortage of available resources to meet essential human needs are among the biggest challenges facing the world. Accurate and up-to-date statistics and information on agricultural capacities form the foundation of proper planning and management in agricultural affairs.
Methods: In this study, Sentinel2-L2A satellite images were initially downloaded, and the Normalized Difference Vegetation Index (NDVI) was extracted using the set of images containing ground reflectance data. Then, the Support Vector Machine (SVM) and Random Forest classification algorithms were applied to the images using the R programming language in the Jupyter Notebook environment.
Results: Finally, it was observed that the Random Forest algorithm performed better and more appropriately, with an overall accuracy of 93% and a kappa coefficient of 87%, compared to the Support Vector Machine algorithm, which had an overall accuracy of 90% and a kappa coefficient of 82%. This preference is due to its higher accuracy and kappa coefficient, indicating a greater agreement with reality and higher prediction accuracy.
Conclusions: The results of these algorithms showed that each algorithm has its own strengths and weaknesses. The Support Vector Machine algorithm is used in many classification problems due to its simple structure and adequate performance. However, in this study, it performed weaker compared to the other algorithm, the Random Forest. The Random Forest algorithm usually provides accurate results due to its ability to combine different models and reduce the effect of overfitting. Nevertheless, its high computational complexity can be problematic in larger applications.
Javad Khoshhal; Abbasali Vali; Moahsen Pourkhosravani
Volume 16, Issue 42 , March 2013, , Pages 139-153
Abstract
Attention to agricultural production capabilities in every region is related to climatic characteristics, so study on the climatic parameters is very important. This study evaluates for assessment the effect of wind on crop conditions and optimizes the amount of crop conditions by wind break on wheat ...
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Attention to agricultural production capabilities in every region is related to climatic characteristics, so study on the climatic parameters is very important. This study evaluates for assessment the effect of wind on crop conditions and optimizes the amount of crop conditions by wind break on wheat in Mohammad Abad in Esfahan. For assessment of changes that are resulting from carminative agronomic Traits of Wheat include: grain weight, number of grains per spike, spikes per square meter, grain yield, biological yield and percentage resting form a complete random design treatments. Distance carminative height was evaluated. Analysis of variance showed significant difference in the level of 1 percent for grain weight, yield and resting percent and 5 percent levels for biological yield and grain number per spike showed. Test to compare mean grain weight, number of grains per spike, spike in m², grain yield, biological yield and percentage resting treatments at different levels indicated significant differences in the averages of each trait in each treatment. Test results compare mean grain weight, number of grains per spike and grain yield indicate a trend similar to the distance from carminative are interactively with increasing distance from the windbreak to 5 times the height of all traits increase. So that significant differences between their control and this is evident from the values of these traits point to reach their maximum and then with increasing amounts of their distance decreases and amounts to 15 times the height of the traits seen carminative without significant difference. For the adjective percentage resting contrast crop plant performance has done so, these traits also influenced by distance are carminative. But the spike in m² and biological function are not affected of distance from the windbreak
