Author: Haroon Khalil
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Bio-inspired optimization algorithms
There are two branches of optimization of problems known as exact methods and heuristics. BIOAs solve heuristic problems by imitating the strategies of nature. There are two main and booming classes in BIOAs. Those are evolutionary algorithms (EAs) and swarm-based algorithms (SBAs). EAs are influenced by the evolution in nature, and SBAs are driven by…
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Optimization in machine learning
ML facilitates systems to recognize patterns from current available algorithms and datasets and develop feasible solution concepts. In ML algorithms, to recognize the patterns it is required to feed the system with the required algorithms and huge data in advance. Then ML carries out some tasks. First it finds, extracts and summarizes the pertinent data.…
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Machine learning in agriculture
ML techniques along with image processing algorithm are used in precision agriculture to increase food production in agricultural fields. Previous data available at different stages of farming are used to predict the conditions to improve production by means of ML algorithms. ML algorithms are useful at different stages of agriculture, such as yield prediction, disease…
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Introduction
With the increase in population, the food production also needs to be increased drastically with the limited available sources. To enhance productivity in farming, so many sophisticated tools are there. Nowadays, internet of things (IoT) and machine learning (ML) are playing a big role in agriculture industry [1, 2].
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Conclusion
Agriculture plays a significant role in improving the economic condition of any country. The crop growth is reduced due to weeds. Earlier the weeds were detected manually, however it is a very expensive and time consuming process. Currently, weed detection is done by robotics, automatic sprayer and weed cutting are thus used. This kind of robotics…
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Neural network classifier
The image data are trained through the convolution neural network. The input is given as the feature-based image which separates the weed and plants in the segmented image. The convolution operation is performed on the matrix of pixels and trained through extracted features. The two layers involved are fully connected and pooling layers. The pooling layer…
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Support vector machine classifier
The machine learning algorithm of SVM is supervised method and used for both regression and classification problems. In SVM algorithm, given plot of image data with n number of features each feature belongs to a particular dimension. Here the two classes are distinguished as weed and as plant. The SVM is a linear hyper plane…
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Feature extraction
The structure represents texture-based local properties of micro-texture and macro textures represent the spatial texture of narrow properties. These properties are not similar between the image pixels. The statistical features based method builds relationship among the gray levels. One-pixel based classifiers known as first order derivative and more than two pixels based classifier is known…
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Proposed interval type II intuitionistic fuzzy c means with spatial triangular fuzzy number
This algorithm decreases various uncertainties in histopathology images. It comprises the following steps: This proposed algorithm detects the weed from various crop images by selecting GLCM features from segmented image. Segmentation of the weed from crops and soil in the input image properly. These results are shown in Figure 3.3. The weed and crops in same…
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Image segmentation
Advanced fuzzy set theory is mostly used in real-time applications such as medical, satellite and agricultural field (Rani and Amsini 2019). In 1975, Zadeh pioneered another advanced fuzzy set called type II fuzzy set. Obviously, membership functions were defined by an expert based on his or her knowledge. These fuzzy set theories were applied to…