Preparation and application of a thidiazuron·diuron ultra-low-volume spray suitable for plant protection unmanned aerial vehicles

Improving the productivity of Xinjiang cotton in heat-limited regions under two life history strategies

Cotton is a major cash crop globally, playing a pivotal role in the textile sector. However, cotton growers in Xinjiang region are experiencing cotton yield penalty caused by limited heat environment. In this region, limited heat conditions strongly arrest cotton plant growth and development resulting in recued productivity. To counteract this problem, there is an urgent need to robustly identify efficient management strategies to improve plant performance and increase cotton yield under heat-limited situations. This will hold crucial implications for agricultural sustainability and global cotton supply. This review article identified challenges faced by cotton producers under heat limited environments with potential solutions to enhance cotton productivity. Specifically, we focused on the implementation of two life history strategies including planting early maturing and cold tolerant cultivars, and adjusting sowing date that can promote early maturity and increase cold stress tolerance. These strategies have shown promising results in protecting cotton plants from limited heat injury and consequently improved cotton productivity. By focusing on Xinjiang province unique climate and associated agronomic practices, valuable insights can be gained, which may have broader applications in other heat-limited cotton-growing regions globally. This comprehensive review endeavors to provide a foundation for future research and practical interventions aimed at boosting cotton yields under limited heat areas.

Comparison of plasma technology for the study of herbicide degradation

The study aimed to investigate the effects of two different plasma systems, including pinhole plasma jet and gliding arc (GA) plasma, for the degradation of herbicide, diuron, in plasma activated solutions (PAS). In the GA plasma system, air was used to generate plasma, however, Ar, oxygen and nitrogen at different gas compositions were compared in the pinhole plasma jet system. The Taguchi design model was used to study the effects of gas compositions. Results revealed that the pinhole plasma jet system was able to degrade over 50% of the diuron in 60 minutes. The optimal plasma generation condition for the highest degradation of diuron used pure Ar gas. The highest degradation percentage of herbicide in PAS corresponded to the lowest hydrogen peroxide (H₂O₂) content, nitrite concentration and electrical conductivity (EC) of the PAS. The diuron degradation products were identified as 3,4-dichloro-benzenamine, 1-chloro-3-isocyanato-benzene and 1-chloro-4-isocyanato-benzene via gas chromatography-mass spectrometry (GC-MS). The GA plasma system was not adequate for the degradation of herbicide in PAS.

Droplet distribution in cotton canopy using single-rotor and four-rotor unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are widely used as the sprayers for low-volume pesticide application in recent years. Droplet distribution characteristics of UAV spraying in the cotton canopy have notable effect on the biological control efficacy of the targets and the defoliation efficiency of the harvest aids. In this work, the influences on droplet distribution in the cotton canopy with respect to the flight height, forward mode, and spraying volume were evaluated by conducting the field trials during two cotton growth stages in 2020, respectively. The first field trial was performed in the cotton flowering stage and the second one was conducted in the early boll development stage. Two typical UAVs equipped with a single-rotor and four-rotor, respectively, were adopted as the spraying platforms in this work. Droplet deposition obtained by water sensitive papers (WSPs) clipped on the cotton leaves was considered as the observing metric. All cotton leaves in the canopy were divided into three groups (i.e., upper, middle, and bottom layers) in both trials. Furthermore, the cotton canopy was divided as eight directions to assess the droplet distribution in the canopy from different directions. The results showed that the droplet deposition varied remarkable between the treatments and in the same canopy within a treatment. The upper layer obtained higher droplet deposition than those of the middle and bottom layers and plants P4 to P8 accessed more droplets than those of the remaining sampling plants in most treatments of both trials for the two UAVs. The upper layer droplet deposition of the four-rotor UAV treatments outperformed that of the single-rotor treatments under the same operating parameters. The forward modes rarely affected the droplet distribution of the four-rotor UAV treatments but significantly influenced that of the single-rotor UAV treatments. For the single-rotor UAV spraying with "head forward", the droplet distribution of the treatment with a flight height of 2 m was more even than that of the 1 and 3 m in the first trial. Under the same flight height, droplet deposition of the treatments with a spraying volume of 22.5 L ha^-1 was remarkably higher than that of the 12 L ha^-1 for both forward modes in the second trial. "Tail forward" of the single-rotor UAV treatment had better penetration at a flight height of 2 m in both trials. Therefore, for the single-rotor UAV, under a flight height of 2 m and a spraying volume of 22.5 L ha^-1, "tail forward" was recommended for applying pesticides to control targets at the lower canopy and "head forward" was a better choice for harvest aid application. Four-rotor UAV was a suitable adoption for the harvest aid application and controlling the targets of the upper canopy. The results also indicate that the systemic pesticides are recommended for UAV spraying due to its uneven droplet distribution uniformity in the whole cotton canopy.

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

Spray droplet size is the main factor affecting the deposition uniformity on a target crop. Studying the influence of multiple factors on the droplet size distribution as well as the evaluation method is of great significance for improving the utilization of pesticides. In this paper, volume median diameter (VMD) and relative span (RS) were selected to evaluate the droplet size distribution under different hollow cone nozzles, flow rates and spatial positions, and the quantitative models of VMD and RS were established based on machine learning methods. The results showed that support vector regression (SVR) had excellent results for VMD (Rc = 0.9974, Rp = 0.9929), while multi-layer perceptron (MLP) had the best effect for RS (Rc = 0.9504, Rp = 0.9537). The correlation coefficient of the prediction set is higher than 0.95, showing the excellent ability of machine learning on predicting the droplet size distribution. In addition, the visualization images of the droplet size distribution were obtained based on the optimal models, which provided intuitive guidance for realizing the uniform distribution of pesticide deposition. In conclusion, this study provides a novel and feasible method for quantitative evaluation of droplet size distribution and offers a theoretical basis for further determining appropriate operation parameters according to the optimal droplet size.