A Review on UAV-Based Applications for Plant Disease Detection and Monitoring

Kouadio, Louis; El Jarroudi, Moussa; Belabess, Zineb; Laasli, Salah-Eddine; Roni, Md Zohurul Kadir; Amine, Ibn Dahou Idrissi; Mokhtari, Nourreddine; Mokrini, Fouad; Junk, Jürgen; Lahlali, Rachid

doi:10.3390/rs15174273

Cited by 26 publications

(2 citation statements)

References 135 publications

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“…As an emerging airborne platform, unmanned aerial vehicles (UAVs) have many advantages, such as low prices, high flexibility, and easy deployment. Therefore, they have been widely used in both civil and military fields, such as precision agriculture [1], building inspection [2], UAV swarm operation [3], and target detection [4]. Meanwhile, UAVs can be integrated into mobile edge computing (MEC) networks as a complement to the terrestrial communication infrastructure [5].…”

Section: Introduction 1background and Related Workmentioning

confidence: 99%

Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning

Lin,

Chen,

2023

Sensors

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With the advantages of real-time data processing and flexible deployment, unmanned aerial vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and military fields. However, due to limited energy, it is usually difficult for UAVs to stay in the air for long periods and to perform computational tasks. In this paper, we propose a full-duplex air-to-air communication system (A2ACS) model combining mobile edge computing and wireless power transfer technologies, aiming to effectively reduce the computational latency and energy consumption of UAVs, while ensuring that the UAVs do not interrupt the mission or leave the work area due to insufficient energy. In this system, UAVs collect energy from external air-edge energy servers (AEESs) to power onboard batteries and offload computational tasks to AEESs to reduce latency. To optimize the system’s performance and balance the four objectives, including the system throughput, the number of low-power alarms of UAVs, the total energy received by UAVs and the energy consumption of AEESs, we develop a multi-objective optimization framework. Considering that AEESs require rapid decision-making in a dynamic environment, an algorithm based on multi-agent deep deterministic policy gradient (MADDPG) is proposed, to optimize the AEESs’ service location and to control the power of energy transfer. While training, the agents learn the optimal policy given the optimization weight conditions. Furthermore, we adopt the K-means algorithm to determine the association between AEESs and UAVs to ensure fairness. Simulated experiment results show that the proposed MODDPG (multi-objective DDPG) algorithm has better performance than the baseline algorithms, such as the genetic algorithm and other deep reinforcement learning algorithms.

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Section: Introduction 1background and Related Workmentioning

confidence: 99%

Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning

Lin,

Chen,

2023

Sensors

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“…However, these studies have only adapted existing mainstream object detectors at a modular level, lacking a systematic and comprehensive design. Moreover, while UAV-based approaches have been employed to monitor over 80 plant diseases, with a focus on leaf diseases and fungal diseases [27,28], to the best of our knowledge, no study has been conducted to detect SPVD using either UAV aerial images or ground images.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Detecting Sweet Potato (Ipomoea batatas) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

Zeng,

Ding,

Song

et al. 2023

Agronomy

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The sweet potato is an essential food and economic crop that is often threatened by the devastating sweet potato virus disease (SPVD), especially in developing countries. Traditional laboratory-based direct detection methods and field scouting are commonly used to rapidly detect SPVD. However, these molecular-based methods are costly and disruptive, while field scouting is subjective, labor-intensive, and time-consuming. In this study, we propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images. We proposed a novel object detector called SPVDet, as well as a lightweight version called SPVDet-Nano, using a single-level feature. These detectors were prototyped based on a small-scale publicly available benchmark dataset (PASCAL VOC 2012) and compared to mainstream feature pyramid object detectors using a leading large-scale publicly available benchmark dataset (MS COCO 2017). The learned model weights from this dataset were then transferred to fine-tune the detectors and directly analyze our self-made SPVD dataset encompassing one category and 1074 objects, incorporating the slicing aided hyper inference (SAHI) technology. The results showed that SPVDet outperformed both its single-level counterparts and several mainstream feature pyramid detectors. Furthermore, the introduction of SAHI techniques significantly improved the detection accuracy of SPVDet by 14% in terms of mean average precision (mAP) in both ground and aerial images, and yielded the best detection accuracy of 78.1% from close-up perspectives. These findings demonstrate the feasibility of detecting SPVD from ground and unmanned aerial vehicle (UAV) high-resolution images using the deep learning-based SPVDet object detector proposed here. They also have great implications for broader applications in high-throughput phenotyping of sweet potatoes under biotic stresses, which could accelerate the screening process for genetic resistance against SPVD in plant breeding and provide timely decision support for production management.

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Next‐Gen Crop Monitoring: MTEG‐RTU Algorithm and UAV Synergy for Precise Disease Diagnosis

Jayachandran

2024

Journal of Chemometrics

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The rapidly changing climatic scenarios are highly favorable for the rising diseases that lead to increasing threats to food production and supply. Various scholars and scientists make long steps to hasten the process of making innovations in farming for managing these issues. In this context, UAV is applied for the purpose of managing and monitoring plant health. The abiotic stresses available in plant diagnosis through traditional strategies are highly labor‐intensive and unfit for large‐scale deployment. Conversely, UAVs designed with mobile sensors, multispectral, radar, and so on make them flexible, affordable, and more effective. Thus, this study proposes a novel meta ensemble transfer extreme gradient‐based random tactical unit (MTEG‐RTU) algorithm for diagnosing crop illnesses precisely. The proposed MTEG‐RTU methodology entails three methods such as transfer learning, adaptive boost, and meta‐ensemble, and the hyper parameters are tuned using random tactical unit algorithm. Healthier and disordered crop images gained from the crop disease dataset comprise 8000 images and are preprocessed. The more optimal features from the preprocessed images are learned through the ResNet method, and these features enter into the classification phase. Random tactical unit algorithm enhanced the performance by optimizing the hyperparameters of MTEG classifier. The experimental results conducted based on the various assessment components and validation dataset indicate that the developed method outperformed the other chosen models, achieving precision, recall, and accuracy of 98.5%, 97.9%, and 98.6%, respectively. The other achievements made by the model are offering technical guidance for conducting the precise diagnosis and treatment of plant pathologies with less time of 9 s.

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A Review on UAV-Based Applications for Plant Disease Detection and Monitoring

Cited by 26 publications

References 135 publications

Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning

Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning

Feasibility of Detecting Sweet Potato (Ipomoea batatas) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

Next‐Gen Crop Monitoring: MTEG‐RTU Algorithm and UAV Synergy for Precise Disease Diagnosis

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