UAV-Based Classification of Cercospora Leaf Spot Using RGB Images

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?

The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (A_F), area of healthy foliage (A_H), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision

BACKGROUND

Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously.

RESULTS

In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers.

CONCLUSION

The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images

Recently, disease prevention in jute plants has become an urgent topic as a result of the growing demand for finer quality fiber. This research presents a deep learning network called YOLO-JD for detecting jute diseases from images. In the main architecture of YOLO-JD, we integrated three new modules such as Sand Clock Feature Extraction Module (SCFEM), Deep Sand Clock Feature Extraction Module (DSCFEM), and Spatial Pyramid Pooling Module (SPPM) to extract image features effectively. We also built a new large-scale image dataset for jute diseases and pests with ten classes. Compared with other state-of-the-art experiments, YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%.

Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).