This paper describes the overview of special issues for the laser applications to agriculture and agricultural products. The topics of this issues cover fluorescence of agricultural products for smart agriculture, on-the-go spectroscopic soil sensing, food fraud detection system using laser gas sensing, plant factory using LEDs, optical control of insects and imaging lidar for vegetation monitoring.

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Smart Agriculture, where big data is collected by sensors connected through IoT and analyzed by AI, is now spreading worldwide. A critically important technology in this is imaging devices; they can extract morphological and spectroscopic features related to the size, shape and quality of agricultural products. Since fluorescent substances are present in most agricultural products, such as protein, free amino acids, vitamins, and organic compounds (including polyphenols, flavonoids and chlorophyll), which have distinctive excitation and emission wavelengths, fluorescent imaging features can act to identify and quantify slight skin injuries, quality characteristics, growth stage, condition, chemical residues, and foreign materials in agricultural, livestock and aquacultural products. To further propel this smart agriculture based on this precise and detailed information, optical systems for fluorescent imaging need to be developed with a detailed understanding of the fluorescent properties of agricultural products as it passes through the production/ supply chain.

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Precision Agriculture (PA) is defined as a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions that can then be based on estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production. This paper describes on-the-go spectroscopic soil sensing systems that provide quick, cost-effective, and accurate measurements for creating maps of soil properties. It also shows how these visualized soil maps assist with farmers’ needs and informs them on what they wish to know about their soil.

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Food fraud detection technology based on stable isotope ratio measurement using laser gas sensing is described. Hydrogen isotope ratio and oxygen isotope ratio have correlation with the latitude of the place where the farm products have grown, and they can be clue to detect the false designation of the origin. Carbon isotope ratio can be used to estimate the addition of the sugar obtained from sugar cane or corn, and it can be clue to detect false designation of ingredients. Since laser gas sensing measurement requires much less techniques or experience than the measurement using conventional mass spectroscopy equipment, it has a potential of being utilized for the verification of authenticity in the middle of supply chain.

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Light-emitting diodes (LEDs) have a narrow light-emitting spectrum, enabling them to focus light on plants with the precise amount of required wavelength of light. Today, many plant factories are growing vegetables with LEDs as a lighting source. Controlling light environments using the monochromatic light of LEDs in plant factories might produce high quality vegetables, for example, leafy lettuce and aromatic herbs, with high contents of nutrient or aromatic components. Such environment control can lead to medicinal plants with rich pharmaceutical ingredients. This report overviews the historical developments of plant factories in Japan using LEDs as a lighting source in Japan and describes the possibilities of technical developments in the near future

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Insects exhibit diverse responses to light. The most typical response is phototaxis, movement towards or away from light. Optical technologies are becoming increasingly effective tools in integrated pest management (IPM). Until the 1990s, insect traps using fluorescent tubes and moth-repellent lights using yellow sodium lamps were widely used. In the 2000s, light-emitting diodes (LEDs) capable of emitting monochromatic light with high luminous efficiency were developed that enhanced the potential of lightbased pest control. At the same time, numerous studies on insect neurophysiology and neuroethology provided new useful information, such as the spectral sensitivity of the insect compound eye and the behavioral responses to specific wavelengths in many species. These efforts led to the discovery of a new biological phenomenon called Light Response Reactions. Recent advances in this area have been astonishing, which motivated the developing new pest control technologies.

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Application of imaging liDAR (IL) for vegetation monitoring is explained in this paper. Structural information of vegetation is often difficult to be measured because it needs laborious tasks. IL can quickly obtain precise 3-D point cloud images of vegetation, so that accurate structural information is obtained efficiently. Rapid progress of automatic driving and Unmanned Aerial Vehicle (UAV) technology is promoting diversification of IL system and thus efficiency of IL data collection and quality of the IL vegetation data have been improved. Besides the structural information, the biochemical and physiological information of vegetation have been measured using IL systems with multiwavelength laser source or image fusion technique between an IL based 3-D image and camera based 2-D images. Suitable features within vegetation images to obtain vegetation characteristics has been able to be extracted using deep leaning technique. This technique can be utilized to IL based 3-D image for extracting useful information of vegetation

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We developed a forage-yield estimation device using a Light Detection and Ranging (LiDAR) sensor on a drone. The drone height was measured with high precision using a real-time kinematic global positioning system (RTK-GPS). We confirmed a high correlation between the estimated grass volume and the dry matter yield from LiDAR with a drone on 3-m height rail. We also measured a wide-area (230 × 50 m squared), grass height distribution from a 10-meter-high automatic drone flight. These are good candidates for future precise agriculture systems.

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