Agriculture is considered necessary to sustain life on this planet. With a growing population, we need agricultural productivity to increase significantly to meet the demands. Image processing has proven to be a powerful tool for analysis in many fields and applications. The agricultural sector, where parameters such as canopy cover, yield, and product quality were important for farmers. It is often the case that professional advice is not affordable, and that the availability of an expert and their services may take time. The situation could be greatly improved if image processing was combined with a reliable communication network, as this would eliminate the need to obtain expert advice within a limited time frame. This can be achieved because image processing is a powerful tool for parameter analysis.
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With continued population growth and increased global demand for food, combined with the unusual change in climate and instability of politics, the industry of agriculture must improve methods to increase field production. This resulted in research for new techniques and technologies that enable farming to get maximum output with minimal input. Image processing is one such technique that can help farmers to detect weeds on farms, control pesticides, observe water usage, and assess the colour of fruits and vegetables. In the late century, weed detection was done manually; special labour was used to detect weeds and plug them with hands from each and every part of the field. Later, herbicides was used to remove weeds by them, but manual labour was still used to detect weeds. After advancements in image processing techniques, they started using image processing for the detection of weeds. Here, we have stated and explained the various image processing and weed detection techniques, and the application of image processing.
Plant diseases that occur in different parts of the plant can be observed by changes in symptoms, colors, spots, etc. Observing these diseases with the naked eye is a typical method, but experts use image processing to identify them in their initial stages and stop them from spreading throughout the field. Color recognition of fruits and vegetables in the food and agriculture industry using image processing and with the help of machine learning has become a popular trend in recent years (Ayman & Ayman, n.d.; Feng & Sun, 2012; Zhang et al., 2014). Color is the main feature in identifying product quality. Color is probably the first parameter that consumers use to determine the quality of food products (Maldonado et al., 2012). Appearance is a factor that consumers use to reject or accept a product. This has a significant impact on product sales. Therefore, in recent years, significant efforts have been made in the field of machine learning to improve food quality. Building a machine that can detect colors like a human being has been a difficult task for the scientific community and industry in recent years (Wabali et al., 2017). An image processing system requires adequate capture conditions. Basic image processing components are:
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Image sensor (complementary metal oxide semiconductor (CMOS).
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Illuminate (fluorescent lamps).
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High contrast background for an interesting subject.
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Frame grabber to capture the real photograph.
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Start the usage of the device to begin taking photos.
Calorimetry is a scientific area that deals with measurement, quantification, and the representation of color. It's very useful in various fields because it has the ability to change color into an objective factor instead of a subjective factor (Collewet & Marchand, 2009). By supplementing this scientific field with the system of computer vision technology, it's possible to evaluate color in a non-invasive, non-contact way and to monitor color properties in a digital and standard way, making this technology suitable for use in food and the agriculture industry for quality assessment. Its versatility allows many other production systems to take advantage of the possibilities offered by colorimetry systems, image processing, and artificial vision.
TopImage Processing
Image processing is the strategy of changing an image into enhanced frames and performing some operation on it. For example, obtaining an enhanced picture or focusing important data on it. This is a type of label setting where the information is an image, similar to a video or photo edge, and the final result can be a picture or the qualities and attributes connected with that picture. These images are usually considered as two-dimensional signals, and a set of signal processing methods are included in image processing.