使用植保无人机(UAV)进行农药喷施是现代农业重要的作业方式。在无人机作业过程中，如何规划无人机的路径，使其最大化的节约资源与能耗，是当前领域研究的一大重点。目前，栅格法是使用频率较高的一种无人机植保路径规划方法。然而，传统栅格法存在方法死板、无法解决凹多边形田块路径规划等问题。本论文在栅格化的基础上，提出了一种不规则田块二值化网格模型。通过该模型可以输出一种二值化网格矩阵，大大提高了解决问题的灵活性。为了求解该模型，提出了一种改进的基于拐点和平面测量技术的非支配排序遗传算法（KPPM-NSGA-ii）。分别建立凸多边形、凹多边形和复杂地形不同情形下的二值化网格模型。实验表明，无论是农药消耗量还是能源消耗量，所提出的KPPM-NSGA-ii算法都比NSGA-ii算法与无规划喷洒方法所得结果更优，该算法节省了能源和农药用量，提高了实际应用的效率。

Alfalfa (Medicago sativa L.) is an important forage crop in the world and it is of great significance for the improvement of its salt tolerance. To improve salt tolerance in alfalfa, a rice ascorbate peroxidase gene (OsAPX2) was introduced into alfalfa using Agrobacterium tumefaciens-mediated transformation with marker gene bar. The different T-DNA insertions in T1 transgenic alfalfa were identified by Southern hybridization. Three independent T2 transgenic lines were selected for stress analysis and the results showed that all of them were salt tolerant compared with wild-type plants. The transgenic plants had low levels of H2O2, malondialdehyde and relative electrical conductivity under salt and drought stresses. Moreover, the contents of chlorophyll and proline, and APX activity were high in transgenic plants under salt and drought stresses. Taken together, the overexpression of OsAPX2 enhances salt tolerance in alfalfa through scavenging reactive oxygen species.