Unmanned Aerial Vehicles in Agriculture: A Survey

Cerro, Jaime del; Ulloa, Christyan Cruz; Barrientos, Antonio; Rivas, Jorge de León

doi:10.3390/agronomy11020203

Cited by 141 publications

(80 citation statements)

References 71 publications

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“…Along with the IoT, the widespread use of autonomous robots such as Unmanned Aerial Robots (UAVs) increases productivity in agriculture. In recent years, studies related to this subject have gained acceleration [21][22][23][24]. In [25], the authors used UAVs to detect possible drainage pipes.…”

Section: Unmanned Aerial Robots' Applications In Agriculturementioning

confidence: 99%

Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

et al. 2022

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The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.

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Section: Unmanned Aerial Robots' Applications In Agriculturementioning

confidence: 99%

Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

et al. 2022

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“…Government bodies are trying to cope with such regulations, but there is plenty of room for growth. In Europe, the European Union Aviation Safety Agency (EASA) presented legislative framework: "Easy Access Rules for Unmanned Aircraft Systems" (Regulations (EU) 2019/947 and (EU) 2019/945), in 2019, for RPA operations and it went into effect from 31 December 2020 [138]. The three principal categories established under this legislation are presented in Table 3.…”

Section: Rpa Application Area Referencementioning

confidence: 99%

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

et al. 2021

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Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

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“…Unmanned aerial vehicles (UAV) have been a promising application for data collection and weed control in the field of agriculture (e.g., broad-acre farming), as demonstrated by Librán-Embid et al [91] and Boursianis et al [92], and, in the future machine vision in agricultural farms, may mainly be used via the UAV. The use of UAVs for creating an accurate map of weeds with DL has been presented by Huang [93], Hunter et al [94], and Cerro et al [95]. UAV images are not only used to obtain general information about the crop.…”

Section: Available Weed Detection Systemsmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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Unmanned Aerial Vehicles in Agriculture: A Survey

Cited by 141 publications

References 71 publications

Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

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