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Here, we present research results on developing a compact multifunctional mobile robotic platform based on tracks. Nowadays, choosing the optimal control system for such equipment remains relevant. We have proposed a system that allows measuring thermal indicators of objects or other environmental parameters in hazardous areas and can be controlled remotely via video channels and a specially developed free-programmable console. We also suggest using a steerable two-axis camera gimbal on a moving platform to implement enhanced VR/AR/FPV technology for efficient robot control. It provides a new quality of operator immersion in control compared to common land-based mobile platforms. With this in mind, we have presented the main points related to the set of components and algorithms of the control system, which is installed directly on the moving platform, and the control system that ensures the operation of the operator panel.

In this study, the optimum temperature and thermal tolerance of postlarvae of the commercially important freshwater prawn Cryphiops (Cryphiops) caementarius were determined after acclimation to six different rearing temperatures (19 °C, 22 °C, 24 °C, 26 °C, 28 °C, and 30 °C) during a 45 day-culture period. Best growth parameter values were obtained within the temperature range of 24 °C to 28 °C, where the optimum temperature for growth was found to be at 26 °C (weight gain 81.70%; specific growth rate 1.33 %/day) but had not significant effect (p > 0.05) on survival (64%–71%) of postlarvae. Increasing the acclimation temperature significantly (p

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy’s water state. This CWC is of great importance for irrigation management decisions. The proposed framework is to integrate visible and thermal imaging data using an artificial neural network as a valuable promising implement for accurately estimating the water content of the plant. The RGB-based features included 20 color vegetation indices (VI) and 6 gray level co-occurrence matrix-based texture features (GLCMF). The thermal imaging features were two thermal indicators (T), namely normalized relative canopy temperature (NRCT) and the crop water stress index (CWSI), that were deliberated by plant temperatures. These features were applied with a back-propagation neural network (BPNN) for training the samples with minimal loss on a cross-validation set. Model behavior was affected by filtering high-level features and optimizing hyperparameters of the model. The results indicated that feature-based modeling from both visible and thermal images achieved better performance than features from the individual visible or thermal image. The supreme prediction variables were 21 features: 14VI, 5GLCMF, and 2T. The fusion of color–texture–thermal features greatly improved the precision of water content evaluation (99.40%). Its determination coefficient (R2 = 0.983) was the most satisfied with an RMSE of 0.599. Overall, the methodology of this work can support decision makers and water managers to take effective and timely actions and achieve agricultural water sustainability.

Heat treatment affects the sensory, biophysical and nutritional properties of milk. To maintain and improve milk quality, an optimization of the heat treatment must be reached to ensure the microbiological safety of the product, while altering as little as possible its sensory and nutritional value. One of the major events occurring upon heating is protein denaturation. The rate of protein denaturation is governed by the immediate chemical environment of the reactants defined by the chemical composition of the system. The aim of the present work was to compare the kinetics of casein denaturation under different conditions, in order to define suitable markers to sensitively assess the degree of reaction in early stages accounting for the time/temperature effects. The results showed a clear dependence of protein denaturation on pH and calcium content, but temperature-time combination did not have a major impact on the extent of protein heat-induced changes. [ABSTRACT FROM AUTHOR]

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy's water state. This CWC is of great importance for irrigation management decisions. The proposed framework is to integrate visible and thermal imaging data using an artificial neural network as a valuable promising implement for accurately estimating the water content of the plant. The RGB-based features included 20 color vegetation indices (VI) and 6 gray level co-occurrence matrix-based texture features (GLCMF). The thermal imaging features were two thermal indicators (T), namely normalized relative canopy temperature (NRCT) and the crop water stress index (CWSI), that were deliberated by plant temperatures. These features were applied with a back-propagation neural network (BPNN) for training the samples with minimal loss on a cross-validation set. Model behavior was affected by filtering high-level features and optimizing hyperparameters of the model. The results indicated that feature-based modeling from both visible and thermal images achieved better performance than features from the individual visible or thermal image. The supreme prediction variables were 21 features: 14VI, 5GLCMF, and 2T. The fusion of color–texture–thermal features greatly improved the precision of water content evaluation (99.40%). Its determination coefficient (R2 = 0.983) was the most satisfied with an RMSE of 0.599. Overall, the methodology of this work can support decision makers and water managers to take effective and timely actions and achieve agricultural water sustainability. [ABSTRACT FROM AUTHOR]