Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit
... Show MoreBurdock ( Arctium lappa), is among the most popular plants in traditional medicine and it is associated with several biological effects. Literature survey revealed the presence of phenylpropanoid compounds .The most widespread are hydroxycinnamic acids ( mainly caffeic acid and chlorogenic acid) and lignans (mainly arctiin and arctigenin). This work will confirm the presence of these compounds in Arctium lappa, cultivated in Iraq, in both root and leaf samples. The dried plant samples were extracted by soxhlet with 80% methanol then separated the main constituents by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC). Identification of the isolated compounds wa
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Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this
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Most below-knee prostheses are manufactured in Iraq without considering the fast progress in smart prostheses, which can offer movements in the desired directions according to the type of control system designed for this purpose. The proposed design appears to have the advantages of simplicity, affordability, better load distribution, suitability for subjects with transtibial amputation, and viability in countries with people having low socio-economic status. The designed prosthetics consisted of foot, ball, and socket joints, two stepper motors, a linkage system, and an EMG shield. All these materials were available in the local markets in Iraq. The experimental results showed t
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Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims
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Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use
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