Droplet spatial distribution of oil-based emulsion spray.

Introduction: Oil-based emulsion solution is a common pesticide formulation in agricultural spraying, and its spray characteristics are different from that of water spraying. The well understanding of its spray characteristics is the theoretical basis to improve the pesticide spraying technology. The objective of the present study is to deepen the understanding of the spray characteristics of oil-based emulsion. Method: In this paper, the spatial distribution characteristics of spray droplets of oil-based emulsion were captured visually using the high-speed photomicrography. On the basis of image processing method, the droplet size and distribution density of spray droplets at different spatial locations were analyzed quantitatively. The effects of nozzle configuration and emulsion concentration on spray structures and droplet spatial distribution were discussed. Results: Oil-based emulsion produced a special perforation atomization mechanism compared with water spray, which led to the increase of spray droplet size and distribution density. Nozzle configuration had a significant effect on oil-based emulsion spray, with the nozzle changed from ST110-01 to ST110-03 and ST110-05; the sheet lengths increased to 18 and 28 mm, respectively, whereas the volumetric median diameters increased to 51.19% and 76.00%, respectively. With emulsion concentration increased from 0.02% to 0.1% and 0.5%, the volumetric median diameters increased to 5.17% and 14.56%, respectively. Discussion: The spray droplet size of oil-based emulsion spray can be scaled by the equivalent diameter of discharge orifice of nozzles. The products of volumetric median diameters and corresponding surface tensions were nearly constant for the oil-based emulsion spray of different emulsion concentrations. It is expected that this research could provide theoretical support for improving the spraying technology of oil-based emulsion and increasing the utilization of pesticide.

Vegetation detection based on spectral information and development of a low-cost vegetation sensor for selective spraying.

BACKGROUND: Real-time site-specific selective spraying for crop protection against weeds, pests and diseases requires fast and precise detection of targets. This study investigated several aspects of vegetation target detection based on spectral information and developed a low-cost vegetation sensor. Specific objectives included: (1) compare vegetation-detection performance using reflected and fluorescence data; (2) evaluate the effect of light source and bandwidth, and select the optimal light source and waveband; and (3) develop a low-cost vegetation sensor and test its performance under different light conditions. RESULTS: The main outcomes of this study are as follows. (1) The fluorescence excited by blue and red light-emitting diodes (LEDs) was an effective attribute with which to differentiate vegetation from non-vegetation and provided results comparative with reflected spectroscopy. (2) A blue LED could excite strong fluorescence and was recommended as the light source. (3) Under illumination by blue and red LEDs, bandwidth did not have any obvious effect on classification accuracy within the studied bandwidths from 0 nm (single wavelength) to 120 nm. (4) A low-cost vegetation sensor was developed and tested, with 100% detection accuracy in both dark and outdoor environments. CONCLUSION: This study suggests that single-waveband fluorescence spectroscopy is an effective approach to detect vegetation targets and low-cost LEDs can be used for illumination. Light source modulation with a sinusoidal signal is an effective way to resist the influence of environmental light. Overall, the findings of this study contribute to an improved understanding of developing low-cost and effective vegetation sensors for selective spraying from theory to application. © 2022 Society of Chemical Industry.

TIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the field.

Introduction: Development of weed and crop detection algorithms provides theoretical support for weed control and becomes an effective tool for the site-specific weed management. For weed and crop object detection tasks in the field, there is often a large difference between the number of weed and crop, resulting in an unbalanced distribution of samples and further posing difficulties for the detection task. In addition, most developed models tend to miss the small weed objects, leading to unsatisfied detection results. To overcome these issues, we proposed a pixel-level synthesization data augmentation method and a TIA-YOLOv5 network for weed and crop detection in the complex field environment. Methods: The pixel-level synthesization data augmentation method generated synthetic images by pasting weed pixels into original images. In the TIA-YOLOv5, a transformer encoder block was added to the backbone to improve the sensitivity of the model to weeds, a channel feature fusion with involution (CFFI) strategy was proposed for channel feature fusion while reducing information loss, and adaptive spatial feature fusion (ASFF) was introduced for feature fusion of different scales in the prediction head. Results: Test results with a publicly available sugarbeet dataset showed that the proposed TIA-YOLOv5 network yielded an F1-scoreweed, APweed and mAP@0.5 of 70.0%, 80.8% and 90.0%, respectively, which was 11.8%, 11.3% and 5.9% higher than the baseline YOLOv5 model. And the detection speed reached 20.8 FPS. Discussion: In this paper, a fast and accurate workflow including a pixel-level synthesization data augmentation method and a TIA-YOLOv5 network was proposed for real-time weed and crop detection in the field. The proposed method improved the detection accuracy and speed, providing very promising detection results.

Nondestructive measurement of kiwifruit firmness, soluble solid content (SSC), titratable acidity (TA), and sensory quality by vibration spectrum.

Maturity is a key attribute to evaluate the quality and acceptability of fruit products. In this study, the impact method was used for nondestructive measurement of kiwifruit maturity. The fruit was vertically dropped onto an impact plate, and an accelerometer was used to measure the response signal. Then, fruit firmness, soluble solid content (SSC), titratable acidity (TA), and sensory scores were measured to determine the kiwifruit maturity. In addition, different modeling methods were proposed for data analysis. The results showed that the optimized prediction results were obtained by the principal component analysis-back-propagation neural network (PCA-BPNN) method for both quantitative and qualitative analysis. The optimized correlation coefficient between prediction and actual values (r p) and root mean square error of prediction (RESEP) for firmness, SSC, TA, and sensory score were 0.881 (2.359N), 0.641 (1.511 Brix), 0.568 (0.023%), and 0.935 (0.693), respectively. The optimized discriminant accuracy for immature, mature, and overmature kiwifruits was 94.2% and 92.1% for calibration and validation, respectively. Such results indicated the feasibility of the proposed impact method for kiwifruit maturity evaluation.

Robust cubature Kalman filter based on variational Bayesian and transformed posterior sigma points error.

An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state-dependent noise is addressed in the iteratively parameter estimation. The new filter not only reduces the uncertainty on sigma points generation but also accelerates the convergence of VB-based noise estimation. The effectiveness of the proposed filter is verified on integrated navigation, and numerical simulations demonstrate that VB-RCKF outperforms VB-CKF and RCKF.

Finite element modeling of light propagation in turbid media under illumination of a continuous-wave beam.

Spatially resolved spectroscopy provides a means for measuring the optical properties of biological tissues, based on analytical solutions to diffusion approximation for semi-infinite media under the normal illumination of an infinitely small light beam. The method is, however, prone to error in measurement because the actual boundary condition and light beam often deviate from that used in deriving the analytical solutions. It is therefore important to quantify the effect of different boundary conditions and light beams on spatially resolved diffuse reflectance in order to improve the measurement accuracy of the technique. This research was aimed at using finite element method (FEM) to model light propagation in turbid media, subjected to normal illumination by a continuous-wave beam of infinitely small or finite size. Three types of boundary conditions [i.e., partial current (PCBC), extrapolated (EBC), and zero (ZBC)] were evaluated and compared against Monte Carlo (MC) simulations, since MC could provide accurate fluence rate and diffuse reflectance. The effect of beam size was also investigated. Overall results showed that FEM provided results as accurate as those of the analytical method when an appropriate boundary condition was applied. ZBC did not give satisfactory results in most cases. FEM-PCBC yielded a better fluence rate at the boundary than did FEM-EBC, while they were almost identical in predicting diffuse reflectance. Results further showed that FEM coupled with EBC effectively simulated spatially resolved diffuse reflectance under the illumination of a finite size beam. A large beam introduced more error, especially within the region of illumination. Research also confirmed an earlier finding that a light beam of less than 1 mm diameter should be used for estimation of optical parameters. FEM is effective for modeling light propagation in biological tissues and can be used for improving the optical property measurement by the spatially resolved technique.

(2,5-Diphenyl-pent-4-yn-1-en-3-yl-idene)triphenyl-phospho-rane.

The title compound, C(45)H(27)P, was obtained as a product of the reaction of triphenyl-methyl-enephospho-rane with one molar equivalent of 1,4-diphenyl-butadiyne in toluene. The compound was very stable under ambient conditions, but rapidly decomposed in solution when exposed to the air. The P atom is tetra-coordinated in an approximately tetrahedral geometry. The length of the C C triple bond [1.206 (2) Å] is in the normal range.