Search

This paper presents a study and analysis of an intelligent and autonomous pesticide spraying robotic system for agricultural applications. This study aims to develop a smart pesticide spraying robotic system, which can move to infected plants in the field, identify the type of disease present in the plant, and spray appropriate pesticide. The robotic system consists of a serial manipulator mounted on a tracked vehicle, vision-based disease detection system and spraying system. The SOLIDWORKS modeling software is used for modeling and assembly of the serial manipulator, tracked vehicle, and spraying setup. The main focus of this article is towards the development of the hydraulic circuit and control system for dealing with multiple diseases present in the plants by making the decisions for regulating the flow of pesticide and switching among the pesticides to identify the most appropriate one. The fuzzy logic-based control system is developed and implemented successfully into the experimental setup with the help of MATLAB Simulink to the Arduino hardware interface.

Physical and biochemical attributes are commonly used for characterization of rice. The physical attributes are related to the quantification of size, shape, colour and texture of the rice grains. Biochemical attributes are assessed from cooking and eating characteristics of rice and are termed like alkali spreading value (ASV), amylose content (AC), gel consistency (GC), grain elongation etc. Estimation of biochemical attributes are often time consuming and require meticulous effort for sample preparation, storage and manual measurement. The gelatinization temperature (GT) is related to Alkali spreading value of rice and is partly associated with the amylose content of the starch. GT has a negative correlation with cooking temperature of rice. In this paper image analysis technique has been proposed for discrimination of rice. A portable flat bed scanner has been used as the imaging device and image analysis software has been developed to measure the rate of dispersion during ASV testing. This machine vision technique is a faster and effective way to determine the ASV. The results obtained are promising towards this new approach for objective estimation of ASV.

This book is written for academic and industry professionals working in the field of sensing, instrumentation and related fields, and is positioned to give a snapshot of the current state of the art in sensing technology, particularly from the applied perspective.The book is intended to give broad overview of the latest developments, in addition to discussing the process through which researchers go through in order to develop sensors, or related systems, which will become more widespread in the future.

Rancidity is a major problem in food systems which occurs through lipid oxidation and significantly affects quality of foods. Owing to the drawbacks of conventional methods of estimating lipid oxidation in foods, alternative inexpensive technology such as electronic nose (e-nose) technology that can accurately and rapidly determine odor profile of foods, is gaining importance. First section of this chapter presents various applications of e-nose analysis of several food systems. Latter section of this chapter focusses on assessment of shelf-life of cookies formulated with un-encapsulated and encapsulated eugenol-rich clove extracts (obtained by supercritical carbon dioxide extraction from clove buds) as a source of natural antioxidant using ALPHA MOS e-nose system and a customized e-nose system. The e-nose results were further validated by conventional methods of rancidity determination. Shelf-life study for a storage period of 200 days revealed that normal cookies without any antioxidant have shelf-life of 100 days when stored in aluminium laminates at \(23 \pm 2^{\,\circ }\mathrm{C}\), while administration of encapsulated clove extract as antioxidant prevented rancidity in cookies for at least 200 days with a shelf-life lead of 100 days over control cookies. These observations were further affirmed by phytochemical analyses which indicated that the addition of encapsulated clove extract in cookies enhanced its nutraceutical potential and antioxidant activity for at least 200 days. Finally, linear regression equations were developed using which rancidity parameters of conventional biochemical assays of cookies can be directly derived from e-nose sensor responses.

Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality by means of modern kernel classifiers using the e-nose signatures. As kernel classifiers, this work investigates the potential of state of the art support vector machine (SVM) classifier and very recently developed nonparallel plane proximal classifier (NPPC) and vector-valued regularized kernel function approximation (VVRKFA) technique of multiclass data classification to build taster-specific computational models. Experimental results show that VVRKFA and one-versus-rest (OVR) SVM models offer high accuracies to predict the considerable variation in tea quality.