Impact of UAVs in Agriculture

Introduction

As stated by ‘Agriculture in 2050 Project,’ it has been estimated that the population will increase to 10 billion by the year 2050. In light of this growth, the food production will be boosted by ~70% and proper water management will be in-disposable (Jha et al., 2019; Kim et al., 2019; Niu et al., 2019). Agriculture is the backbone as it serves as the primary source of food production. However, there are many challenges in this area such as predicting the type of soil, weather forecasting, crop outcome, monitoring water levels, scheming a proper irrigation plan, detecting crop diseases (Daponte et al., 2019; Pawar et al., 2020), type of soil and removal of pests and weeds (Ju & Son, 2018; Kulbacki et al., 2018; Lakshmi & Naresh, 2018). Many techniques are being currently used for enhancing agricultural productivity. Precision agriculture is one of these modern farming practices that can make production more efficient and effective by perceiving, measuring and reacting to intra as well as inter-field crop variability. The incorporation of UAV-based technology will reduce farmers’ expenditure along with the benefit of being more user-friendly and viability in the long run.

Initially, precision agriculture was implemented through satellites; however, obtaining satellite data is not conventionally simple or easily economically viable. Moreover, the data so obtained from these recourses is incomprehensible to farmers for direct application due to the fact that the majority of the rural places possess deficient telecommunications infrastructure. Therefore, the need for an easily accessible technique that can work in any location, regardless of internet availability, is required. Thus, the UAVs are being considered as an alternative to replace the manmade aircrafts (Shruthi et al., 2019) to extend the potential of the smart and precise data accumulation and analysis with economic viability. The UAVs have significantly decreased the amount of time for the same which results in an increase in stability, accuracy, and productivity (Kim et al., 2019). The main advantage of UAVs is that can navigate through the 3D space in tailor made trajectories according to the specific data requirements. Depth sensors (Bhushan et al., n.d.; R. Goel et al., 2020; Kumar et al., 2023; Samant et al., 2021) are incorporated so as to facilitate the generation of maps and 3D templates. They are relatively easy to operate and provide temporal as well as high spatial resolutions with a wide spatial coverage.

UAVs and their control systems together constitute the Unmanned Aerial System (UAS) which primarily relies on the utilization of Machine Learning (ML) techniques (Bhushan et al., 2023; Kedia & Bhushan, 2022; Nalavade et al., 2020; Rana & Bhushan, 2022; Singh & Bhushan, 2022; Suri et al., 2022; Verma et al., 2022) through which high resolution image data is obtained at more frequent periodic intervals when compared to images taken by the satellite. It is important to check the quality of data (Bhushan et al., 2020; Bhushan, Ángel Galindo Duarte, et al., 2021; Bhushan, Kumar, et al., 2021; S. Goel, 2012; Negi & Kaur, 2017) which is being used for the application of ML techniques as this aspect directly affects the results derived from the data so collected. Due to its peculiar feature or ability of capturing high resolution imagery data (Norasma et al., 2019; Rao & Rao, 2019) below cloud levels, UAVs are used to collect Very High Resolution (VHR) images thereby providing an economically effective data analysis techniques along with sensing capabilities (Feng & Li, 2019; Lu, 2019). There are different aspects where UAVs especially in precision agriculture as it comprises of various processes such as soil mapping, water management, weather monitoring, etc.