Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

BACKGROUND: The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. RESULTS: The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. CONCLUSION: The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

Evaluationof compact air-induction flat fan nozzles for herbicide applications: Spray drift and biological efficacy.

Nozzles are the most critical component of a sprayer for pesticide applications. Recently, air-induction nozzles and twin flat-fan air-induction nozzles have started to be used for herbicide applications. In order to evaluate the potential of compact air-induction nozzles for herbicide spraying, this paper compares the effects of air-induction nozzles and standard flat-fan nozzles on spray atomization, deposition, drift, and weed control efficacy in maize and wheat. Droplet spectra were measured by a laser particle size analyzer, and drift potential values were determined using a drift test bench (ISO 22401). A field study was conducted to compare the spray drift and biological efficacy between Lechler standard flat-fan nozzles and compact air-induction nozzles including different nozzle sizes. In the range from 0.2 to 0.4 MPa, the droplet size classes of the LU and ST nozzles were very similar and ranged from fine to very fine, while the droplets of the air-induction nozzles IDK and IDKT were medium or coarse depending on the spray pressure and nozzle size. The drift potential trials showed that the droplet size characteristics, mainly V 100, are strongly linked with the drift reduction potential. Both drift potential and field results showed that the compact air-induction nozzles had a good performance in drift reduction. In terms of weed control biological efficacy, there were no significant differences between standard flat-fan nozzles and air-induction nozzles. In all cases, the efficacy values were above 80% both in maize and in wheat. In conclusion, air-induction nozzles are recommended for herbicide applications as they provide good biological efficacy while significantly reducing the amount of spray drift, which is of great significance for the protection of the environment and the surrounding sensitive crops.

Comprehensive assessment of intelligent unmanned vehicle techniques in pesticide application: A case study in pear orchard.

The intelligent pesticide application techniques in orchards have grown rapidly worldwide due to the decrease in agricultural populations and the increase in labor costs. However, whether and how intelligent pesticide application techniques are better than conventional pesticide application remains unclear. Here, we evaluated the performance of the unmanned aircraft vehicle (UAV) and unmanned ground vehicle (UGV) on pesticide application, ecological environment protection, and human's health protection compared to conventional manual methods. We quantified characteristics from the aspects of working effectiveness, efficiency, environmental pollution, water saving and carbon dioxide reduction. The results showed that the UAV application has the advantages of a higher working efficiency and less environmental pollution and natural resource consumption compared to the UGV and conventional manual methods despite of its worse spray performance The UGV application techniques could improve spray performance at the cost of high environmental pollution. The conventional spray gun technique was unfriendly to environmental and resource protection although it showed a better spray performance. Thus, the balance of improving spray performance and controlling environmental pollution is the key to improve the performance of UAV and UGV technology in the future. The study could be useful in the development of intelligent pesticide application techniques and provide scientific support for the transition of intelligent management in orchards.

Development of multifunctional unmanned aerial vehicles versus ground seeding and outplanting: What is more effective for improving the growth and quality of rice culture?

The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs.

Design and development of orchard autonomous navigation spray system.

Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system "perception-decision-control," the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Utilization of thermal infrared image for inversion of winter wheat yield and biomass.

The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation (drip irrigation, sprinkler irrigation, flood irrigation). It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass. The temperature of crop and background was measured by thermal infrared image. It is necessary to get the crop background separation index (CBSI(L), CBSI(H)), which can be used for distinguishing the crop value from the image. CBSI(L) and CBSI(H) (the temperature when the leaves are wet adequately; the temperature when the stomata of leaf is closed completely) are the threshold values. The temperature of crop ranged from CBSI(L) to CBSI(H). Then the ICWSI was calculated based on relevant theoretical method. The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI. In order to construct the high accuracy simulation model, the samples were divided into two parts. One was used for constructing the simulation model, the other for checking the accuracy of the model. Such result of the model was concluded as: (1) As for the simulation model of soil moisture, the correlation coefficient (R2) is larger than 0.887 6, the average of relative error (Er) ranges from 13.33% to 16.88%; (2) As for the simulation model of winter wheat yield, drip irrigation (0.887 6, 16.89%, -0.12), sprinkler irrigation (0.970 0, 14.85%, - 0.12), flood irrigation (0.969 0, 18.87%, -0.18), with the values of R2, Er and CRM listed in the parentheses followed by the individual term. (3) As for winter wheat biomass, drip irrigation (0.980 0, 13.70%, -0.13), sprinkler irrigation (0.95, 13.15%, -0.14), flood irrigation (0.970 0, 14.48%, -0.13), and the values in the parentheses are demonstrated the same as above. Both the CRM and Er are shown to be very low values, which points to the accuracy and reliability of the model investigated. The accuracy of model is high and reliable. The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.

[SVM-based spectral recognition of corn and weeds at seedling stage in fields].

A handheld FieldSpec 3 Spectroradiometer manufactured by ASD Incorporated Company in USA was used to measure the spectroscopic data of canopies of seedling corns, Dchinochloa crasgalli, and Echinochloa crusgalli weeds within the 350-2 500 nm wavelength range in the field. Each canopy was measured five times continuously. The five original spectroscopic data were averaged over the whole wavelength range in order to eliminate random noise. Then the averaged original data were converted into reflectance data, and the unsmooth parts of reflectance spectral curves with large noise were removed. The effective wavelength range for spectral data process was selected as 350-1 300 and 1 400-1 800 nm. Support vector machine (SVM) was chosen as a method of pattern recognition in this paper. SVM has the advantages of solving the problem of small sample size, being able to reach a global optimization, minimization of structure risk, and having higher generalization capability. Two classes of classifier SVM models were built up respectively using "linear", "polynomial", "RBF"(radial basis function), and "mlp (multilayer perception)" kernels. Comparison of different kernel functions for SVM shows that higher precision can be obtained by using "polynomial" kernel function with 3 orders. The accuracy can be above 80%, but the SV ratio is relatively low. On the basis of two-class classification model, taking use of voting procedure, a model based on one-against-one-algorithm multi-class classification SVM was set up. The accuracy reaches 80%. Although the recognition accuracy of the model based on SVM algorithm is not above 90%, the authors still think that the research on weeds recognition using spectrum technology combining SVM method discussed in this paper is tremendously significant. Because the data used in this study were measured over plant canopies outdoor in the field, the measurement is affected by illumination intensity, soil background, atmosphere temperature and instrument accuracy. This method proposes a kind of research ideology and application foundation for weeds recognition in the field.

(2S,8Z)-2-butyroxy-8-heptadecene: major component of the sex pheromone of chrysanthemum gall midge, Rhopalomyia longicauda.

The sex pheromone of the chrysanthemum gall midge, Rhopalomyia longicauda (Diptera: Cecidomyiidae), the most important insect pest in commercial plantations of chrysanthemum, Dendranthema morifolium (Ramat.) Tzvel., in China, was identified, synthesized, and field-tested. Volatile chemicals from virgin females and males were collected on Porapak in China and sent to the United Kingdom for analysis. Coupled gas chromatographic-electroantennographic detection (GC-EAG) analysis of volatile collections from females revealed two compounds that elicited responses from antennae of males. These compounds were not present in collections from males. The major EAG-active compound was identified as 2-butyroxy-8-heptadecene by gas chromatographic (GC) retention indices, mass spectra, in both electron impact and chemical ionization modes, hydrogenation, epoxidation, and derivatization with dimethyldisulfide. The lesser EAG-active compound was identified as the corresponding alcohol. The ratio of butyrate to alcohol in the collections was 1:0.26. Racemic (Z)-8-heptadecen-2-ol and the corresponding butyrate ester were synthesized from (Z)-7-hexadecenyl acetate, and the synthetic compounds found to have identical GC retention indices and mass spectra to those of the natural, female-specific components. Analysis of the volatile collections on an enantioselective cyclodextrin GC column showed the natural pheromone contained (2S,8Z)-2-butyroxy-8-heptadecene. Field tests showed that rubber septa containing racemic (Z)-2-butyroxy-8-heptadecene were attractive to R. longicauda males. The (naturally occurring) S-enantiomer was equally as attractive as the racemate, while the R-enantiomer was not attractive to males, and did not inhibit the activity of the S-enantiomer. The attractiveness of the butyrate was significantly reduced by the presence of even small amounts of the corresponding alcohol.