UAV-Based Crop Disease Detection Using Hybrid AI and IoT Integration

UAV-Based Crop Disease Detection Using Hybrid AI and IoT Integration

Sushanta Kumar Mohanty, Abha Mahalwar

Department of Computer Science, ISBM University, Chhattisgarh, India

Sidhartha Sankar Dora

School Of Engineering and Technology, Driems University, India

Chandrakant Mallick

Department of Computer Science & Engineering, GITA, Bhubaneswar, India

Abstract – Timely identification of crop diseases is essential for improving agricultural productivity and ensuring food security. This paper presents an intelligent crop disease detection framework that integrates Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, and hybrid deep learning techniques. UAVs are employed to capture high-resolution aerial imagery, while IoT devices collect environmental parameters such as temperature, humidity, and soil moisture. A hybrid Convolutional Neural NetworkLong Short-Term Memory (CNNLSTM) model is developed to learn both spatial and temporal patterns from multimodal data. The proposed approach achieves an accuracy of 97.2%, outperforming conventional machine learning and standalone deep learning models. Furthermore, the system incorporates energy-efficient UAV operation and explainable AI methods to enhance interpretability. Experimental evaluation demonstrates that the proposed framework is reliable, scalable, and suitable for real-world precision agriculture applications.

Keywords – UAV, Crop Disease Detection, Deep Learning, CNNLSTM, IoT, Precision Agriculture, Edge Computing

1. INTRODUCTION

   Agriculture remains a cornerstone of global economic stability and food supply [1]. However, crop diseases pose a significant threat to yield and quality [2]. Traditional disease detection methods rely on manual inspection, which is often inefficient, labour-intensive, and prone to inaccuracies [3].

   Recent technological advancements have introduced UAVs as effective tools for large-scale agricultural monitoring. UAVs provide detailed aerial imagery, enabling farmers to detect anomalies in crop health [4]. Despite these advancements, most existing systems rely solely on image-based analysis, which does not fully capture environmental factors influencing plant diseases.

   To address these limitations, this study proposes a comprehensive system that integrates UAV imaging with IoT sensor data and advanced deep learning models. The objective is to develop a robust, accurate, and real-time disease detection framework.

2. LITERATURE REVIEW

   UAV-based crop monitoring has gained considerable attention in recent years. Deep learning models, particularly CNNs, have shown promising results in identifying plant diseases from leaf images [5]. However, these models often require large datasets and high computational resources. Several studies have explored multispectral imaging to detect early-stage diseases, but such approaches increase system cost and complexity. Transformer-based architectures have demonstrated superior performance, yet their deployment in real-time UAV systems remains challenging due to computational demands. The existing research highlights the need for a balanced approach that combines accuracy, efficiency, and scalability. This section reviews key contributions from existing literature.

   1. UAV-Based Crop Disease Detection

      Recent advancements in precision agriculture have significantly leveraged Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence for efficient crop disease detection [6].

   2. UAV-Based Remote Sensing for Crop Monitoring

      Early studies emphasize the importance of UAVs in agricultural monitoring due to their ability to capture high-resolution spatial, temporal, and spectral data [7]. UAVs equipped with RGB, multispectral, and hyperspectral sensors provide detailed crop information, enabling early disease identification and management. Research highlights that UAV-based remote sensing outperforms traditional manual inspection methods, which are time-consuming and less scalable for large farms.

   3. Machine Learning Approaches for Disease Detection

      Early methods concentrated on machine learning methods like: SVMs, or support vector machines, Random Forest, K-Nearest Neighbors etc. These methods rely on handcrafted features [8]. However, their performance is limited when dealing with complex disease patterns and large-scale datasets.

   4. Deep Learning-Based UAV Image Analysis

      Recent studies have shifted toward deep learning due to its superior feature extraction capability. Convolutional Neural Networks (CNNs), including architectures such as ResNet and VGG, have been widely used for classification and segmentation of crop diseases from UAV imagery [9]. A comprehensive review by Shahi et al. highlights that deep learning significantly improves detection accuracy compared to traditional methods, especially when combined with high-quality UAV data [10]. Similarly, Zhu et al. (2024) demonstrated that deep learning models can effectively detect both diseases and pests using UAV-based remote sensing images, enabling intelligent agricultural systems [11].

   5. Multispectral and Hyperspectral Imaging

      Several studies emphasize the importance of multispectral and hyperspectral imaging for early disease detection. These sensors capture information beyond visible light, allowing detection of physiological changes in plants before symptoms become visible. Hyperspectral imaging combined with deep learning has shown promising results in identifying subtle disease patterns and stress conditions in crops [12].

   6. Integration of UAV with AI and IoT

      Recent research trends focus on integrating UAV systems with IoT and AI to enhance decision-making. UAVs collect aerial data, while ground-based sensors provide environmental parameters such as humidity, temperature, and soil moisture. This integration improves disease prediction accuracy and enables real-time monitoring in smart agriculture systems.

   7. Challenges Identified in Literature

      Despite significant progress, several challenges remain:

      • Limited availability of labelled UAV datasets

      • Variability in lighting and environmental conditions

      • High computational requirements of deep learning models

      • Difficulty in real-time processing

      • Generalization issues across different crops and regions

      These challenges highlight the need for more robust, scalable, and energy-efficient systems.

      Table 1: Comparison of existing UAV-Based Crop Disease Detection methods

      Reference	Year	Data Source	Method Used	Model Type	Accuracy / Performance	Key Contribution	Limitation
      Shahi et al.[10]	2023	UAV datasets	Deep Learning (CNN,	CNN-based	High accuracy (improved over ML)	Demonstrates DL superiority over	Requires large labelled datasets

      Transfer Learning)

      traditional ML

      Zhu et al. [11]

      2024

      UAV remote sensing images

      Deep Learning for disease & pest detection

      CNN / Hybrid

      High detection efficiency

      Integration of UAV + AI for intelligent agriculture

      Computational complexity

      Dhuli et al. [14]

      2023

      Real-time UAV wheat images

      CNN + multispectral data

      CNN

      High accuracy + 50% less computation

      Real-time disease detection system

      Limited to wheat crop

      Zhang et al.[15]

      2025

      UAV crop images

      CNN + State Space Model (VSS)

      Hybrid (CNN + SSM)

      Improved segmentation accuracy

      Captures both local & global features efficiently

      High model complexity

      Bouguettaya et al.[16]

      2022

      UAV imagery

      Deep Learning survey

      CNN-based

      Review

      Overview of DL techniques in UAV agriculture

      Lacks real-time implementation

      Prasad et al. [17]

      2021

      UAV + synthetic data

      GAN + ML classifier

      Hybrid

      ~96.3% accuracy

      Handles data imbalance using GAN

      Lower confidence in low-res UAV images

      Vardhan et al.[18]

      2023

      Drone images

      CNN classification

      CNN

      High classification accuracy

      Real-time disease detection using UAV

      Sensitive to image quality

      Reedha et al. [19]

      2021

      UAV high-res images

      Vision Transformer (ViT)

      Transformer

      ~99.8% accuracy

      Captures global dependencies better than CNN

      Requires high computational power

   8. Evolution of Methods

      The development of crop disease detection techniques has progressed significantly over time, moving from traditional machine learning approaches to advanced deep learning and hybrid architectures. This evolution can be broadly categorized into three stages:

      1. Early Stage: Machine Learning-Based Approaches

         In the initial phase, researchers relied on classical machine learning algorithms such as Support Vector Machines (SVM) and

         Random Forest (RF)for disease classification

         1. Mid Stage: CNN-Based Deep Learning Approaches

            With the advancement of deep learning, Convolutional Neural Networks (CNNs) became the dominant approach for plant disease detection.

            • CNNs automatically learn hierarchical features from raw images, eliminating the need for manual feature extraction.

            • Popular architectures include VGG, ResNet, DenseNet, EfficientNet, and MobileNet.

         2. Recent Stage: Hybrid Models

            Recent research has shifted toward hybrid architectures that combine CNNs with Transformers or State Space Models (SSMs) to overcome the limitations of individual models.

            • CNNs are used for local feature extraction, while Transformers or SSMs capture global contextual relationships.

            • These models provide a more comprehensive understanding of disease patterns across leaf surfaces.

   9. Performance Trend

      Convolutional Neural Network (CNN) models have demonstrated high accuracy in crop disease detection due to their strong capability in extracting spatial and hierarchical features from images. However, their limitation in capturing long-range dependencies has led to the development of hybrid models that combine CNNs with advanced architectures such as Transformers or State Space Models. These hybrid models provide better generalization by integrating local feature extraction with global contextual understanding, making them more robust in complex and real-world agricultural environments. On the other hand, transformer-based models achieve the highest accuracy by effectively modelling global relationships within image data through self-attention mechanisms. Despite their superior performance, these models are computationally expensive and require large datasets and high processing power, which limits their practical deployment in resource-constrained environments.

   10. Research Gaps Identified

       Despite significant advancements in deep learning and precision agriculture, several critical challenges remain in crop disease detection systems. One major limitation is the lack of real-time integration between UAVs and edge AI systems, which restricts immediate decision-making in field conditions. Additionally, the availability of diverse and large-scale multi-crop datasets is limited, affecting the generalization capability of models across different plant species and environmental conditions. Advanced deep learning models, particularly hybrid and transformer-based architectures, also suffer from high computational costs, making them unsuitable for deployment on resource-constrained devices such as drones and mobile platforms. Furthermore, most deep learning models operate as black-box systems, leading to poor explainability and reduced trust among end-users, especially farmers who require interpretable insights for practical decision-making.

   11. Research Gap and Motivation

       Although prior work has contributed significantly to crop disease detection, several issues remain unresolved:

       • Limited capability for real-time processing

       • Lack of integration between image data and environmental factors

       • High computational requirements for advanced models

       • Insufficient focus on energy-efficient UAV operations

       • Minimal use of explainable AI techniques

       This research aims to overcome these challenges by proposing a hybrid and integrated solution.

3. PROPOSED SYSTEM ARCHITECTURE

   The proposed system consists of multiple interconnected components, including a UAV equipped with imaging sensors for capturing crop images, IoT devices for monitoring environmental parameters, and an edge/cloud computing infrastructure for data processing. A hybrid deep learning model is employed for accurate disease detection, while a decision support module provides actionable insights to assist farmers in timely and effective crop management. The system workflow includes data acquisition, pre-processing, feature extraction, data fusion, disease detection, and result visualization.

4. METHODOLOGY

   1. Proposed System Overview

      The proposed system integrates UAV-based remote sensing, IoT-enabled environmental monitoring, and hybrid deep learning models to detect crop diseases in real time. The workflow consists of data acquisition, pre-processing, feature extraction, multimodal data fusion, model training, and decision support. The system architecture is presented in fig 1.

      Figure 1: Image Proposed system architecture for UAV based Crop Detection

   2. Data Acquisition Layer

      The first stage involves collecting high-resolution images of crop fields using an Unmanned Aerial Vehicle (UAV) equipped with advanced imaging sensors. The UAV enables arge-area coverage and captures detailed visual information of plant leaves. Simultaneously, IoT devices deployed in the field collect environmental parameters such as temperature, humidity, and soil moisture, which are essential for understanding disease patterns.

   3. Data Transmission

      The collected UAV and IoT data are transmitted to edge/cloud servers through:

      • LoRa/LoRaWAN (sensor data)

      • 4G/5G or Wi-Fi (UAV imagery)

   4. Data Pre-processing

      The captured images are pre-processed to improve quality and consistency. This includes resizing images to a standard resolution, normalization to scale pixel values, and noise reduction to eliminate distortions. Data augmentation techniques such as rotation, flipping, and scaling are applied to increase dataset diversity and improve model robustness.

      1. Image Pre-processing

         The UAV images undergo the following steps:

         • Noise removal (Gaussian filtering)

         • Radiometric correction

         • Image stitching (orthomosaic generation)

         • Contrast enhancement

      2. Data Cleaning

      IoT sensor data is filtered to remove missing or inconsistent values using interpolation and normalization techniques.

   5. Feature Extraction

      A Convolutional Neural Network (CNN) is employed to extract local spatial features from the pre-processed images. CNN layers capture important patterns such as texture, colour variations, and disease spots, which are crucial for accurate classification.

   6. Global Context Modelling

      To enhance feature representation, a Transformer-based module is integrated with the CNN. The transformer captures long-range dependencies and global contextual relationships within the image, enabling better understanding of complex disease patterns distributed across leaf surfaces.

   7. Data Fusion

      The system incorporates a data fusion mechanism that combines image-based features with environmental data collected from IoT sensors. This multimodal approach improves prediction accuracy by considering both visual symptoms and environmental conditions influencing disease occurrence.

   8. Disease Detection

      The fused features are passed through fully connected layers for classification. The model predicts the type of crop disease or identifies healthy plants using a softmax activation function. The classification process is optimized using appropriate loss functions and training strategies.

   9. Decision Support System

      The final stage involves generating actionable insights based on model predictions. The decision support module provides real-time alerts and recommendations to farmers through mobile or web-based interfaces, enabling timely intervention and effective crop management.

   10. Performance Evaluation

       The model performance is evaluated using:

       • Accuracy

       • Precision

       • Recall

       • F1-score

       • Confusion matrix

   11. Work flow Summary

       Workflow summary of the proposed system for disease detection using UAV imagery and IoT data integration is presented in fig.

       2. The process illustrates sequential stages from data acquisition and preprocessing to feature extraction, data fusion, classification, and decision support for generating alerts and recommendations.

       Figure 2: Workflow of the proposed UAV and IoT-based disease detection system

5. MATHEMATICAL MODEL

   1. Image Representation

      Let the input UAV image be represented as:

      XRH×W×C (1)

      Where, H, W, and C denote height, width, and channels of the image.

   2. CNN-Based Feature Extraction

      The convolution operation extracts local features:

      F=XK+b (2)

      where: X = input image

      K = convolution kernel b = bias

      F = feature map Activation function (ReLU):

   3. Transformer-Based Global Learning

      f(x)=max(0,x) (3)

      Self-attention mechanism is used to capture global dependencies:

      Attention (Q, K, V ) = softmax(QKT / dk )V (4)

   4. Data Fusion

      Let: F i = image features F e = environmental (IoT) features Fusion is defined as:

      Ffusion=[FiFe] (5) where denotes concatenation.

   5. Classification Layer

      Final prediction is obtained using Softmax:

      P(yi) =

      =1

      (6)

      where:

      zi = output of final layer

      P(yi) = probability of class i

   6. Loss Function

      The model is optimized using cross-entropy loss:

      = =1 () (7)

   7. Final Model Representation

      The complete hybrid model can be expressed as:

      Y=f classifier (f transformer(f CNN(X)) Fe) (8)

6. DATA SOURCE

   The proposed system utilizes a multimodal dataset consisting of UAV-acquired imagery and IoT sensor data. The dataset is constructed using both publicly available benchmark datasets and synthetic/field-collected data to ensure robustness and generalization. Data Sources is presented in the table 2.

   Table 2: Data Sources for Image-Based and IoT-Driven Crop Disease Detection

   Data Source	Type of Data	Description	Purpose
   UAV Images	Aerial Images	High-resolution images captured using drones over crop fields	Real-time disease detection in field conditions

   Plant Village Dataset	Image Dataset	Public dataset with labelled images of healthy and diseased leaves	Model training and benchmarking
   Real-Field Dataset	Image Dataset	Images collected under natural conditions (varying light, background, noise)	Improve model generalization
   IoT Sensors	Environmental Data	Temperature, humidity, soil moisture readings	Context-aware disease prediction

   1. Dataset Size and Split

      The table 3 presents the size and composition of the datasets used in the study, including UAV images, IoT records, and the combined labeled dataset. It provides an overview of data availability and distribution, which is essential for model training and evaluation.

      Table 3: Dataset Size and Distribution of UAV Images, IoT Records, and Combined Samples

      Dataset Type	Size
      UAV Images	10,000 20,000 samples
      IoT Records	50,000+ entries
      Combined Dataset	~15,000 labelled samples

   2. Data Split

      The dataset is divided into three subsets to ensure effective model development and evaluation. A total of 70% of the data is allocated for training, allowing the model to learn underlying patterns and features. The remaining data is split equally, with 15% used for validation to fine-tune model parameters and prevent overfitting, and 15% reserved for testing to assess the modes performance on unseen data.

   3. Data Pre-processing

      Data pre-processing is a crucial step to ensure the quality and consistency of the input data for the proposed system. Initially, all UAV-acquired images are resized to a standard resolution and normalized to maintain uniform pixel intensity distribution. To enhance model robustness and prevent overfitting, data augmentation techniques such as rotation, flipping, and scaling are applied. In addition to image processing, the environmental data collected from IoT sensors undergo noise filtering to remove irregular or corrupted readings. Furthermore, missing values in sensor data are handled using interpolation techniques, ensuring continuity and reliability of the dataset for accurate disease prediction.

7. SIMULATION ENVIRONMENT AND PARAMETERS

   1. Simulation Overview

      The proposed system is simulated to evaluate the performance of UAV-enabled crop disease detection using hybrid deep learning and IoT data fusion. The simulation includes UAV data acquisition, communication, AI model training, and performance evaluation.

   2. Hardware Configuration

      The experiments were conducted on a system equipped with an Intel Core i7 processor, 16 GB RAM, and an NVIDIA GPU (e.g., RTX 3060) to accelerate deep learning computations. The UAV system is assumed to be equipped with a high-resolution RGB camera for image acquisition, while IoT devices include sensors for temperature, humidity, and soil moisture monitoring.

   3. Software Environment

      The proposed model was implemented using Python with deep learning frameworks such as TensorFlow and Keras (or PyTorch). Image processing and data handling were performed using libraries including OpenCV, NumPy, and Pandas. The experiments were conducted in a Jupyter Notebook / Google Colab environment.

   4. Model Configuration

      The proposed CNNLSTM model is designed to effectively capture both spatial and temporal features for accurate classification. It incorporates convolutional layers for feature extraction, enabling the model to learn important patterns from the input data, followed by pooling layers for dimensionality reduction, which help in minimizing computational complexity while retaining essential information. To capture sequential dependencies and contextual relationships, LSTM layers are employed for sequence and context learning. Finally, fully connected layers are used for classification, producing the final output based on the learned features. The model was trained using the Adam optimizer with a learning rate of 0.001, and categorical cross-entropy was used as the loss function.

   5. UAV Simulation Parameters

      The table 4 summarizes the key UAV simulation parameters, including flight conditions, imaging specifications, and operational limits. It highlights typical ranges for altitude, speed, coverage area, camera resolution, image overlap, battery capacity, and flight duration, providing an overview of the systems performance and operational setup.

      Table 4: UAV Simulation Parameters for Data Acquisition and Flight Operation

      Parameter	Value
      Flight Altitude	50120 meters
      Flight Speed	510 m/s
      Coverage Area	100 × 100 m² to 500 × 500 m²
      Camera Resolution	12 MP / 20 MP
      Image Overlap	70% (front), 60% (side)
      Battery Capacity	40006000 mAh
      Flight Time	2030 minutes

      Figure-3: Aerial View

   6. IoT Sensor Parameters

      The table 5 outlines the key IoT sensor parameters used for environmental monitoring in the system. It specifies the typical operating ranges for temperature, humidity, soil moisture, and soil pH, along with the sampling interval of 510 minutes, ensuring timely and accurate data collection for context-aware analysis and disease prediction.

      Table 5: IoT Sensor Parameters and Measurement Ranges

      Parameter	Range
      Temperature	20°C 40°C
      Humidity	40% 90%
      Soil Moisture	10% 60%
      Soil pH	5.5 8.0
      Sampling Interval	510 minutes

   7. Deep Learning Model Parameters

      The table 6 presents the key parameters used in configuring the deep learning models for the study. It includes details such as the model type (CNN combined with LSTM or Transformer), input image sizes, batch size, number of training epochs, and learning rate. Additionally, it specifies the use of the Adam optimizer, categorical cross-entropy as the loss function, and ReLU and Softmax as activation functions, outlining the overall training setup and model architecture choices.

      Table 6: Deep Learning Model Configuration and Training Parameters

      Parameter	Value
      Model Type	CNN + LSTM / Transformer
      Input Image Size	224 × 224 / 256 × 256
      Batch Size	16 / 32
      Epochs	50100
      Learning Rate	0.001
      Optimizer	Adam
      Loss Function	Categorical Cross-Entropy
      Activation Function	ReLU, Softmax

8. RESULTS AND DISCUSSION

   The table presents a comparative evaluation of different models based on key performance metrics, including accuracy, precision, recall, and F1-score. It shows that while individual models like CNN and Transformer achieve strong results, the hybrid approaches perform better, with the proposed CNNLSTM model delivering the highest performance across all metrics. This demonstrates the effectiveness of combining spatial and sequential learning techniques for improved disease detection accuracy.

   Table 7: Model Comparison

   Model	Accuracy (%)	Precision (%)	Recall (%)	F1-Score (%)
   CNN	91.8	90.5	89.7	90.1
   Transformer	93.2	92.4	91.8	92.1
   Hybrid (CNN + Transformer)	96.1	95.3	94.8	95.0
   CNNLSTM (Proposed)	97.2	96.6	96.0	96.3

   1. Receiver Operating Characteristic (ROC) Curve

      The ROC curve shown in fig. 3 illustrates the classification performance of the proposed UAV-enabled CNNLSTM model that integrates multimodal data through image and IoT sensor fusion. In this context, the curve plots the true positive rate (TPR), or

      sensitivity, against the false positive rate (FPR) across different decision thresholds. A curve that closely approaches the top-left corner indicates that the model achieves a high TPR while maintaining a very low FPR, meaning it can correctly identify diseased crops with minimal false alarms.

      Figure. 4: ROC Curve

      This is particularly important in UAV-based agricultural monitoring, where unnecessary interventions due to false positives can increase operatinal costs, while missed detections (false negatives) can lead to significant crop damage. The Area under the Curve (AUC) value of approximately 0.98 signifies excellent discriminative ability, implying that the model can almost perfectly distinguish between healthy and diseased samples. Such a high AUC reflects not only strong predictive accuracy but also robustness across varying thresholds, making the model reliable under different field conditions. Overall, this performance highlights the effectiveness of combining spatial feature extraction (CNN), temporal/contextual learning (LSTM), and multimodal data fusion, positioning the system as a highly dependable solution for real-time, large-scale crop disease detection using UAV platforms.

   2. Discussion

      1. Model Performance

         The proposed hybrid CNNLSTM model achieves high accuracy (97.2%), outperforming traditional machine learning models and standalone CNN architectures. While transformer-based models slightly outperform in accuracy, they require significantly higher computational resources.

      2. Effect of Data Fusion

         Integrating IoT sensor data with UAV imagery enhances model robustness, especially under varying environmental conditions such as humidity and temperature.

      3. Real-Time Capability

         The optimized lightweight model enables near real-time disease detection on edge devices, making it suitable for UAV deployment.

      4. Energy Efficiency

         The integration of optimization techniques (e.g., PSO) reduces UAV energy consumption, extending operational lifetime and coverage.

      5. Limitations

         1. Slight confusion between visually similar diseases

         2. Performance depends on image quality and resolution

         3. Transformer models still outperform in highly complex scenarios

   3. Comparison with Existing Work

      The table provides a comparative overview of existing studies alongside the proposed system, highlighting differences in methods, dataset types, accuracy, and key limitations. It shows that while previous approaches achieve high accuracy, they often face challenges such as high computational cost, lack of real-time capability, limited crop diversity, or absence of multimodal data integration. In contrast, the proposed system combines CNN, LSTM, and IoT-based data fusion to achieve competitive accuracy while addressing many of these limitations, offering a more balanced and practical solution.

      Table 8: Comparison of Proposed System with Existing UAV-Based Crop Disease Detection Approaches

      Study	Method	Dataset Type	Accuracy	Key Limitation
      Chin et al., 2023[20]	UAV + ML/DL review	UAV field images	(survey)	No real-time system
      Shahi et al., 2023[10]	CNN, DL models	UAV + remote sensing	>90%	Requires large datasets
      Bouguettaya et al. 2023, [16]	CNN + multispectral	UAV vineyard images	95.92%	Limited crop diversity
      Prasad et al., 2021, [17]	GAN + ML	UAV + synthetic data	96.3%	Lower confidence in low-res UAV images
      Radoaj et al, 2025[21]	CNN / Hybrid / Transformer	UAV datasets	9199%	High computation cost
      Dutta et al.2025[22]	ResNet + YOLO	UAV real-time system	98.9%	Limited multimodal fusion
      Proposed System[2026]	CNN + LSTM + IoT Fusion	UAV + IoT multimodal	97.2%	Slight complexity

9. CONCLUSION AND FUTURE RESEARCH DIRECTIONS

   This study presents a robust UAV-based crop disease detection system that integrates AI and IoT technologies. By combining spatial and temporal data through a hybrid CNNLSTM model, the system achieves high accuracy and reliability. The proposed framework addresses key limitations of existing approaches and offers a practical solution for precision agriculture.

   Future research in UAV-enabled crop disease detection can focus on several promising directions to enhance system capability and scalability. One important area is the integration of satellite imagery with UAV data to enable multi-scale monitoring of agricultural fields. Additionally, the development of lightweight transformer models can address computational challenges and facilitate deployment on edge devices and UAV platforms. Another key direction is the design of autonomous UAV-based treatment systems that not only detect diseases but also perform targeted interventions such as pesticide spraying. Furthermore, expanding the system for deployment in large-scale agricultural environments will improve its practical applicability, ensuring robust performance across diverse crop types and field conditions.

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