Author: Haroon Khalil
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Statistical models as machine learning techniques
Statistics is a branch of mathematics based on mathematical techniques applied to data. From the prehistoric times, statistics has provided efficient problem-solving methodologies. The major operations of statistics are collecting and reviewing data, analysing and interpreting data and showing results in summarized manner (Kumar and Choudhry 2010). Some of the statistical methods useful in machine…
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Life cycle of data analytics
Data analytics life cycle consists of six stages to solve problems and taking better decision according to the data. Like data mining, analytics also follows a prescribed channel to handle the data. The phases include discovering the problem, preparing data, model planning, model building, visualization of results and operationalization (Poudel 2016) (Figure 2.4).
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Data analytics
Data analytics and advanced mining technologies analyse data with exploratory data analysis which is used to describe data and establish a pattern and relationship in the data with the help of statistical methods. Confirmatory data analysis evaluates data using statistical methods. Data analytics analyse the data in the manner described in Figure 2.2. Data can be…
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Rainfall and crop production
Rainfall is the base variable for crop production. Meteorological department estimates the rainfall level of India. There are six regional meteorological centres throughout India. India is the highest producer of crops. Crops are divided based on seasons such as rabi, kharif and zaid crops. Rabi crops are harvested during the months of September–October. Kharif crops…
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Companies associated with agriculture sector
Companies associated with agriculture sector have great importance in the 21st century because any country depends upon its agriculture sector as it generates big revenue that increases the wealth of the country and hence boosts its economy. New automation techniques are used to boost agriculture sector (see Table 1.1). Company Specialization Location Year Description Blue River…
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Advantages and disadvantages in agriculture
Generally, in machine learning it is not easy to analyse the unstructured data. For this type of data analysis, applying deep learning methods will be more useful where we can use different types of data formats to make algorithm work. To find the relation between different domains which are interdisciplinary we can use deep learning…
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GAN
GAN is considered as one of the most useful neural networks in many fields. Mainly GAN is used to find the feature loss in image processing caused by down sampling. When the image is compressed, some of the information may get lost or quality of that image is lost, so we may need to recover…
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RNN
Land cover classification is the key challenging area in agriculture, it involves recognizing the type and quality of land. In the past, a lot of applications were based on mono-temporal observation. Mono-temporal methods are dependent on some factors like weather. To solve the problems related to RNN, a model known as NARX is introduced, where…
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CNN
CNN is broadly used in agriculture since it has strong capacity for image processing. Major applications of deep learning in agriculture can be classified as plant or crop classification, pest and yield prediction, robot harvesting, monitoring of disaster etc. Mainly disease recognition model for plants can be applied by leaf image and pattern classification. Berkley…
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Application of deep learning in agriculture
Deep learning has transformed agriculture sector to its new level. Deep learning uses techniques such as conventional neural network, RNN and GAN. This gives better results and encourages agriculture domain. The procedure of deep learning uses processing of images and studying the information with efficient results. The intense growth in deep learning field has shown…