Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Shahi, Tej Bahadur; Xu, Cheng-Yuan; Neupane, Arjun; Guo, William

doi:10.3390/rs15092450

Cited by 88 publications

(27 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Investigating the detection of diseases in various crops using sensors in UAVs analysed by ML and deep learning (DL) methods, including CNNs, Tej Bahadur Shahi et al [20] found that ML methods categorised agricultural diseases with 72-98% accuracy, and DL methods achieved 85-100%. This highlights the potential of UAVs in early disease detection to mitigate crop loss, despite challenges related to high-resolution image usage and UAV imaging variability.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Singh,

Pratap,

Mehta

et al. 2024

IoT

View full text Add to dashboard Cite

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Singh,

Pratap,

Mehta

et al. 2024

IoT

View full text Add to dashboard Cite

show abstract

“…Recently, in the agricultural sector, plant diseases have become a major factor that caused the reduction of productivity and loss of yield. To minimize these losses number of researchers have done several works to detect disease at an early stage by identifying the infected leaf automatically [14], [28], [29]. There are vast works done for the identification of plant diseases using image processing, machine learning approach, and deep learning approach.…”

Section: Related Workmentioning

confidence: 99%

Developing Common Bacteria Blight Disease Detection Model for Common Bean Using Deep Learning Approach

Senbato,

Ayalew

2023

Preprint

View full text Add to dashboard Cite

In many farm locations in Ethiopia, common bacterial blight (CBB), one of the common bean's known diseases, impacts the crop, lowers yield by up to 45 percent of the production, and also has an impact on seed quality. Recently, many researchers have tried to overcome this disease by conducting field surveys, crop management activities, crop rotation, chemical treatment, proper cultural practices, and integrated disease management. However, this method is a very tedious, time-consuming, and costly technique that requires more experts. To overcome these issues, a modern deep learning approach is proposed for the early detection of common bean disease. The three main phases are followed for the proposed approach. Firstly, the collection of healthy and diseased common bean images with the help of domain experts from different agricultural research centers is done. Then the design of a modern convolutional neural network that can detect and classify the input image as diseased or healthy is done. Lastly, the designed model is trained and evaluated. During the classification, the designed model is assessed against the performance of two pre-trained models (VGG16 and InceptionV3) to accurately detect the common bean disease. The performance of the proposed model is evaluated using a dataset that contains total images of 3,135 with two classes of healthy and common bacterial blight disease. We have used data augmentation techniques to generate more images to fit the proposed model. Based on our experimental results, the best performance is achieved using a proposed model with a classification accuracy of 98.2% for early detection of common bacterial blight disease on a common bean leaf. The result of this study can help farmers and domain experts by detecting CBB disease at an early stage for the necessary treatments.

show abstract

“…These research studies highlight the application of deep learning and machine learning techniques for various tasks in the context of unmanned aerial vehicles (UAVs) and remote sensing. Shahi and colleagues [23] focus on the identification of crop diseases using UAV-based remote sensing, emphasizing the role of image processing methods, assessing the effectiveness of ML and DL techniques, and exploring future research directions in UAV-based crop disease detection and classification [24,25]. Behera, Bakshi, and Sa [26,27] present lightweight CNN architectures for real-time segmentation and object extraction on IoT edge devices, achieving high performance on datasets suitable for urban and agricultural mapping, as well as road damage detection using YOLO algorithms.…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Fayaz,

Nam,

Dang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch. Transfer learning also facilitates the effective utilization of limited labeled data for fine-tuning, making it a valuable strategy for optimizing model accuracy in complex urban land classification tasks. Moreover, we strategically employ fine-tuned versions of Inception-v3 and DenseNet121 networks, emphasizing the transformative impact of these architectures. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. Through experiments conducted on the UC-Merced_LandUse dataset, we demonstrate the effectiveness of our approach, achieving remarkable results, including 92% accuracy, 93% recall, 92% precision, and a 92% F1-score. Moreover, employing heatmap analysis further elucidates the decision-making process of the models, providing insights into the classification mechanism. The successful application of CNNs in LAC, coupled with heatmap analysis, opens promising avenues for enhanced urban planning, agricultural zoning, and environmental monitoring through more accurate and automated land-area classification.

show abstract

Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Cited by 88 publications

References 121 publications

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Developing Common Bacteria Blight Disease Detection Model for Common Bean Using Deep Learning Approach

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Contact Info

Product

Resources

About