The Relationship between Droplet Density and the Defoliation Effect of Cotton Harvest Aids.

Spraying harvesting aids is an important step before the mechanical harvesting of cotton. To clarify the direct relationship between the droplet density and the defoliation effect of cotton harvest aid solutions, we evaluated the relationship between the droplet density and the defoliation effect. The determination method and evaluation standard of the number of droplets required per square centimeter to achieve 50% leaves defoliation (DN50) of the harvest aid solution were further explored. The results revealed a linear relationship between the droplet density and the cotton defoliation rate when the spraying volume was 22.5 L/ha and the harvest aid dosage was 1/2 and 2/3 of the recommended dosage. When the harvest aid dosage was 5/6 and 1 times the recommended dosage, the relationship between the droplet density and the defoliation rate of cotton was logarithmic. The DN50 of the low-concentration harvest aid solution (450 L/ha) was significantly higher than that of the high-concentration solution (22.5 L/ha). The addition of spray adjuvant Beidatong significantly reduced the DN50 of cotton harvest aids. The field experiment showed that the droplet density increased with the increase of the spraying volume sprayed by unmanned aerial vehicles. There was a positive correlation between the spraying volume and the defoliation effect after changes in the cotton harvest aid dosage. When the dosage of Mianhai (MH) was 5/6 of the recommended dosage, the defoliation effect at the spraying volumes of 22.5, 27.0, and 30.0 L/ha reached the peak values at 71.54, 78.56, and 83.23%, respectively. This study proposed the concept of DN50 and its determination method. The fitting equations between the droplet density and defoliation effect and between the harvest aid concentration and defoliation effect were established to provide a theoretical basis for the scientific spraying of cotton harvest aid solutions.

Study on Droplet Impact and Spreading and Deposition Behavior of Harvest Aids on Cotton Leaves.

The deposition and spreading of pesticide droplets on the surface of plants is a severe challenge to precise pesticide application, which directly affects the pesticide utilization rate and efficacy. Cotton harvest aids are widely used in machine-picked cotton but the effect of formulation and concentration on the droplet behavior and defoliation effect of cotton defoliants is not clear. To clarify the influence of formulation and concentration on the droplet behavior of cotton defoliants, four formulations (suspension concentrate (SC), water dispersible granule (WG), oil dispersion (OD), and wettable powder (WP)) of cotton defoliants were used to prepare different concentrations of harvest aid solutions, according to the spraying volume. The physicochemical properties, droplet impact, and spreading and deposition behavior were studied. The results indicated that the four kinds of harvest aids have good physicochemical properties and can be wet and spread on cotton leaves. The surface tension of the high-concentration harvest aid solution (the spraying volume was less than 1.2 L/667 m2) was increased, which increased the contact angle and reduced the adhesion tension, adhesion work, and the spreading area. Once the harvest aid solution systems impacted the cotton leaves, it could spread to the maximum in a short time (10 ms). The field experiment showed that the droplet spectrum of harvest aids changed slightly, the coefficient of variation (CV) did not exceed 50%, and the defoliation rate was better when the spraying volume was 1.5 L/667 m2. The correlation and principal component analysis showed that the spraying volume (concentration) and coverage were negatively correlated with the defoliation rate, while the viscosity, diffusion factor, and spreading rate were positively correlated with the defoliation rate. Overall, the use of appropriate spraying volume application in cotton fields can improve the performance of spray, increase the effective deposition and wetting spread of defoliants on cotton leaves, further reduce the dosage of defoliants, and improve pesticide utilization. These results can provide a theoretical basis for the scientific preparation and spraying of cotton harvest aid solutions.

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image.

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R 2 was 0.9109, and RMSE was 0.91277 t.ha-1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.