Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

几个世纪以来，农业是几个共和国的主要职业。然而，由于个体从农村迁移到城市，农业存在预防。现代技术在精准农业领域的使用提高了生产力，也改善了一个国家的出口。由于不同的环境因素、疾病和昆虫对作物的侵袭，尤其是西红柿，生产力可能会受到影响。目标区域（即受环境因素影响的作物区域）识别和及时向地面站传送作物病害信息是进行预防性测量的必要条件。在飞行传感器网络中，由于能量限制、通信范围、拓扑频繁变化、链路过期和高移动性。在本文中，我们提出了多个无人机的定位和聚类，用于识别番茄作物田中受影响的目标区域。无人机的定位取决于环境因素的权重，即相对湿度、土壤湿度、温度、光照强度、NPK（氮（n）、磷（p）和钾（k））和氢的功率（ pH值）。使用蜜蜂优化方法定位和形成多个无人机集群，以准确识别目标区域。我们的仿生方法的性能在通信开销、数据包传输率、使用现有的基于群体智能的方案并通过模拟验证的平均端到端延迟和能源消耗。仿真结果表明，仿生方法在所选方法中表现更好。