Author: Haroon Khalil
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Image preprocessing
An image Z with M rows and N columns and its intensity level range is measured as a collection of fuzzy singletons. The intensity range is in between L, 0 to 1. The membership value μik with color intensity xn xm is considered. The contrast intensification operator was introduced by Zadeh in 1973. This operator is fully dependent…
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Image acquisition
The dataset comprises 60 images and it is accessible online. These pictures are taken from real-time robot bonirob. The images are of carrot plants for detecting inter- and intra-weeds (Haug and Ostermann, 2014). All these images are taken for the processing and provide better results for detection of the areas of weeds and plants.
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Literature survey
An exhaustive research was done on several papers describing various methods adopted for weed detection. These papers were summarized as follows. Image processing techniques and machine vision are broadly used in various fields such as agriculture industry or for detection of an object. The images are mathematically represented as rows and columns with red. So,…
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Objectives of proposed work
Machine learning algorithms are used for plant differentiation and weed detection with accuracy. These algorithms are used in real-time applications of nondestructive analysis of image objects.
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Robust weed detection in image processing
The major benefits of automatic based weed systems which help to reduce the labor price and the usage of pesticides. Also, these systems locate the weeds in crops in an efficient way. In a survey done in Australia, it is reported that farmers spend about 1.5 billion dollars every 12 months for weed control activities. This is…
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Problem of the statement
During the past years, weed detection was done physically by humans. Afterward with the innovation in technology, herbicides came into use to expel the weeds. Then image processing came into use for weeding. In this chapter, detection of weeds in the crop using image processing will be focused on. Manual weed sampling is time and…
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Introduction
The agriculture field is working to save our environment by making certain exceptional changes in its practices to secure crop production. This approach is totally based on technology which can support soil preparation, planting and weed eliminating process. The most vital problems surface due to weeds which increase the biological competition with existing crop such…
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Forecasting of ARIMA
Forecasting of rainfall from 2016 to 2025 can be done with low and high levels of expectation. The confidence levels are 80% and 95%. Figure 2.14 shows the graphical representation of forecasting (Table 2.5). Year Point forecast Low 80 High 80 Low 95 High 95 2016 1103.422 969.6137 1237.231 898.7797 1308.065 2017 1113.926 979.6177 1248.235 908.5191 1319.333…
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Measuring goodness of fit
The components AIC, AICc and BIC are the estimators of best fit model of ARIMA. These coefficients are supported to maximize the log likelihood value of ARIMA model. Rainfall data provide best fit along with (0, 1, 1) × (1, 0, 0) model and its coefficients are smaller than the other ARIMA models (Table 2.4). ARIMA model…
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Autocorrelation and partial autocorrelation functions
Figure 2.12 shows the autocorrelation and partial autocorrelation of rainfall data. These are the plots used to display the correlated data with the significant level. In the figure, the data are correlated within the boundary level with 95% confidence interval significant level. Partial autocorrelation is the relationship between the observed data which has applied time series and…