Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holland Scientific Crop Circle Sensor ACS 470 (HSCCACS-470) and 430 (HSCCACS-430). Rainfall data, with or without including crop height, improved the YP models in term of reliability and consistency. The polynomial model was relatively better compared to the exponential model. A significant difference in the relationship between sensor reading multiplied by rainfall data and crop yield was observed in terms of soil type, clay and medium textured, and cultivation system, conventional and no-till, respectively, in the North Dakota corn study. The two potato sites in Maine, irrigated and dryland, performed differently in terms of total yield and rainfall data helped to improve sensor YP models. In conclusion, this study strongly advocates the use of rainfall data while using sensor-based N calculator algorithms.
A field experiment was conducted in Al-Yusufiya district - Al-Mahmoudiya district, Baghdad province during the winter season 2021, to study improving the efficiency and management of water use and the productivity of lettuce under different irrigation systems. The Nested-Factorial Experiments design was used, where the main plots include the first factor, irrigation levels (I1) 50%, (I2) 75%, (I3) 100, (I4) 125%, (I5) 150% ETpan. After depleting 35% of the available water and in terms of climatic data from the American Evaporative Basin, Class A. Then the main factor is divided into three replicates, and the coefficients of the second factor are distributed randomly within each replicate, which includes the irrigation system: surface drip i
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The aim of the current research is to identify the effect of the active thinking model in the achievement of students of the fifth grade applied science of physics, and their pivotal thinking by verifying the two zero hypotheses, where there is no significant difference at the level of significance (0.05) between the average scores of the experimental group who studied physics using the active thinking model and the average scores of the control group students who studied the same material in the usual way in the achievement test, as well as in the pivotal thinking test. The research sample consisted of (77) students of the applied fifth grade students in two divisions (a) and (b), randomly selected (a) to be the experimental group, and (b)
... Show MoreObjective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using
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Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.
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