Acquisition of entomological data with high-frequency lidar is an emerging research field in rapid development. The technique offers very high numbers of observations per time unit, suitable for statistical models. In this work, we use a near-infrared Scheimpflug lidar with a sampling frequency of 3.5 kHz to assess the activity of free flying organisms. In-situ measurements were done during the rainy season in Ivory Coast, and hierarchical cluster analysis was used to quantify the amount of unique modulation signatures. Here we propose a method to estimate the number of observed species within a certain air volume for a given time span. This paves the way for rapid in-situ biodiversity assessment in accordance with recent priorities for protection of pollinator diversity during global changes.
Minimizing insecticide use, preventing vector diseases and facilitating biodiversity assessments are suitable applications of recent advances in photonic insect surveillance and entomological lidar. The tools also comprise a new window into fundamental aspect of the fascinating life and ecology of insects and their predators in situ. At the same time, it is evident that lidars are subject to finite detection range given by the instrument noise and saturation levels, and therefore, intervals of the biomass spectra are sectioned at different ranges. The Scheimpflug lidar allows an interesting trade-off between high sample rate and low pulse energy for retrieving wing beat harmonics and slow sample rates with high pulse energy for detecting small species far away. In this paper, we review and revise calibration, sizing and associated deficiencies, and report count rates to 10 4 insects/minute up to 2 km range. We investigate if and how high dynamic range can be exploited in entomological lidar and also how fast and slow sample rates could complement each other and capture a wider span of the biomass spectrum. We demonstrate that smaller insect can be detected further away by long exposures and show consistency between the captured biomass size spectra. However, we find unexpected discrepancies between short and long exposures in the range distributions. We found that vertebrates as well as specular insects can saturate signals. Error sources and limitations are elaborated on.
In agricultural sector, maturity is the main decision criterion for starting the harvest. This criterion is usually revealed by a number of parameters such as pH, sugar, dry matter, water and vitamin C, which are informative but technically tedious to measure. The cashew apple is the hypertrophied peduncle which is attached to the cashew nut. It is a nutritious (very juicy fruit (85 to 90% water), sweet (7 to 13% carbohydrates), acidic and vitamin C content) fruit with high therapeutic and medicinal properties. The cashew apple is used as a raw material for many industrial applications (juice and alcohol). This research was conducted as a preliminary step towards the development of a real-time remote sensing technique for assessing the quality of tropical fruits. Spectral acquisitions were carried out from intact cashew apple using optical system composed reflector coupled with spectrometer USB 4000 FL from Ocean Optics (350-1100 nm). Immediately after spectral acquisition, the samples were analyzed by using chemical methods (sugar content, dry matter content, water content, vitamin C and pH). Preprocessing treatment method, bootstrap method was required to create statistical new samples and to increase the number of samples required. This method was used to improve the predictive performance of calibration model. Statistical models of prediction were developed using an artificial neural network (ANN) method. The results obtained from the models built by ANN showed strong relationships between predicted and experimental values: (Rsquare = 0.9870, RMSE= 0.0262) for pH, (Rsquare=0.9869, RMSE=0.1392) for Sugar, (Rsquare=0.9726, RMSE=0.3333) for water content, (Rsquare=0.9703, RMSE=0.3464) for vitamin C and (Rsquare=0.9922, RMSE= 5.0304, RMSE=5.0304) for dry matter. These results confirm the potential of visible spectroscopy to predict quality parameters of cashew apples remotely and make decisions about best harvest time
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