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.
To know the effect of bio-enhancer (zeolite), biohealth, mineral fertilizers and their interactions, the possibility of replacing mineral fertilizers with bio-enhancers and bio-enhancers, and their effect on some potato yield measurements. A field experiment was conducted at one of the field stations of the College of Agricultural Engineering Sciences, University of Baghdad, near the electronic calculator center, research station (F) in Al-Jadriya region in the loam mixture soil during autumn season 2021-2022 AD, It was designed using a completely randomized block design (RCBD) with three replicates. The factors of the study experiment included three levels of zeolite (0, 6 tons ha-1, and 12 tons ha-1), which were symbolized by (Z0)
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Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. According to the proposed method, the images were firstly filtered using the wiener filter then the active contour algorithm could b
... Show MoreThe present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea
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Modified optical fiber sensors received increasing attention because of their superior properties over electrical sensors. These properties include their immunity towards electromagnetic interference and the ability to be deployed in corrosive and volatile environment. Several optical fiber platforms have been developed for chemical sensing applications based on modifying optical fiber cladding layer such as etched, tapered, D-shaped and etched-tapered. The modifications purpose is to extend the evanescent wave propagating out of the core physical dimensions. Thus, evanescent wave interaction with analyte is enhanced. Modified optical transducing platforms are integrated in gas sensing applications, such as ammonia. Modified optical
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The experiment was conducted at field of garden of Department of Biology, Collage
of Education (Ibn-Al-Haitham) University of Baghdad during winter season of 2009-2010.
The aim of present study is the effect of growth regulator Gibberellins by using two
concentrations (100, 200) ppm and also Thiamine in two concentrations (10, 50) ppm, on the
some yield component characters and active component of volatile oil Cumin (Cuminum
cyminum L.).
The results showed that GA3 in (100) ppm increased the yield component, protein
concentration and increased in Cuminaldehyde, Perillaldehyde and Thyoml concentration.
The results showed that the best concentration was (50) ppm of Thiamine showed an
increasing concentratio