Soft Computing Application in Precision Agriculture

Abstract

Computer based technologies have made great impact on the methodologies newlineused in many real world sectors. Many areas like medical, agriculture are highly newlinerelying on computers for data collection and analysis to engender fruitful information newlinethat can be used in decision making and reforms in management practices in relate newlinedomain. Chapter 1 introduces computer vision and soft computing concepts. It also newlinedescribes precision agriculture, a modern paradigm used in agriculture, considers newlineentire farm as collection of small units and find irregularities present in production newlineand requirements for those units. Ultimate goal of PA is to reduce overlay for newlinecultivation to increase profit. newlinePresent study is conducted to implement computer vision and soft computing newlinetechnologies for production estimation in floriculture. Real world Polyhouse scenes newlineare digitized using digital camera and used in the study. Study has designed and newlinedeveloped object detection, classification and quality assessment systems for Gerbera newlineflowers using various algorithms from mentioned technologies. In chapter 2, object newlinedetection system target to detect object of interests i.e. matured and immature flowers newlinefrom grayscale and color images using histogram slicing, threshold segmentation, newlineunsupervised clustering and soft computing artificial neural network technology. newlineIn chapter 3, detected objects are sorted into grown flowers and immature newlinebuds to determine current and advance production of flowers with classifiers based on newlineback propagation neural network. Different classifiers on color, size and shape newlinefeatures are designed to classify objects into desired output classes. newlineChapter 4 gives quality assessment system that implements Fuzzy Inference newlineSystem to grade Gerbera flower based on flower head size, stem length and stem newlinestrength parameters. Digital Image Processing methods are used to extract these newlineparameters from the digital image of selected Gerbera flower. All extracted newlineparameters are then passed through fuzzification, inferenc