Applications Of Image Processing And Soft Computing Systems In Agriculture
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Applications of Image Processing and Soft Computing Systems in Agriculture
The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.
IoT and WSN Applications for Modern Agricultural Advancements: Emerging Research and Opportunities
Currently, the demand by consumption of agricultural products may be predicted quantitatively; moreover, the variation of harvest and production by the change of a farm's cultivated area, weather change, disease, insect damage, etc. is a challenge that has led to improper control of the supply and demand of agricultural products. Advancements in IoT and wireless sensor networks in precision agriculture and the cloud computing technology needed to deploy them can be used to address and solve these issues. IoT and WSN Applications for Modern Agricultural Advancements: Emerging Research and Opportunities is an essential research book that focuses on the development of effective data-computing operations on agricultural advancements that are fully supported by IoT, cloud computing, and wireless sensor network systems and explores prospective applications of computing, analytics, and networking in various interdisciplinary domains of engineering. Featuring a range of topics such as power monitoring, healthcare, and GIS, this book is ideal for IT practitioners, farmers, network analysts, researchers, professionals, academicians, industry experts, and students.
Soft Computing for Image Processing
Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.