Real Time Digital Image Processing of Agricultural Data

Real Time Digital Image Processing of Agricultural Data

Real Time Digital Image Processing of Agricultural Data
In my earlier articles, I had discussed about about application of Big data for gathering Insights on green revolution and witnessed about a research work on supply chain management using big data analytics on agriculture. Incrementally, got an opportunity to implement data science methodology (a game theory approach) to make the results of SCM as an incentive compatible one. However, in this article I am trying to discuss about a large scale digital image processing obtained using time-series photographs of agricultural fields and sensor data for parameters, that should be done parallely with the help of Big Data Analytics such that the result of this work can facilitate SCM process exponentially.

We are focusing on using deep learning and machine learning techniques for identifying patterns for making predictins and decision making on large-scale stored / near real-time data sets. By this, we can identify the crop type, quality, maturity period for harvesting, early identification of bugs and diseases, soil quality attributes, early identification of need for soil nourishments etc., on a larger farms. These automation reduces considerable amount of manual work on large farms. For this,we need to work with two kinds of images that is fetched on near-real-time by sensors; i.e: vector and bitmap. The vector image (represented by mathematical vectors) where resizing image at any scale can be achieved without loss of quality, However the bitmap image is achieved by means of mapped bits where the image pixels are organized as a series of rows and columns formed by pixels (as a pixels’ matrix) with each pixel (picture element) has only one color. There is a necessity for using intelligent deep learning methodology on BigData such that on each iteration, the machine itself grasps the minute details on patterns that enables the system to give accurate results over the period of time. We can exploit the ability of Deep Learning for extracting large-scale, high-level, complex abstraction on given data sets along with the data representations especially unsupervised data (such as our digital imagery data), that makes it as a valuable analysis on Big Data. More specifically, we can classify the problem areas in a large agricultural field by means of image tagging, using automated semantic indexes, data tagging, fast information retrieval then discriminative modeling, which can be better addressed with the aid of Deep Learning along with automated machine learning. However traditional machine learning and feature extraction algorithms are not efficient enough for extracting the complex and non-linear patterns generally observed in large scale images on Big Data sets.

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