C. Bravo

Bio: C. Bravo is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Hyperspectral imaging & Plant disease. The author has an hindex of 12, co-authored 19 publications receiving 1425 citations. Previous affiliations of C. Bravo include Catholic University of Leuven.

The potential of optical canopy measurement for targeted control of field crop diseases

Abstract: There is increasing pressure to reduce the use of pesticides in modem crop production to decrease the environmental impact of current practice and to lower production costs. It is therefore imperative that sprays are only applied when and where needed. Since diseases in fields are frequently patchy, sprays may be applied unnecessarily to disease-free areas. Disease control could be more efficient if disease patches within fields could be identified and spray applied only to the infected areas. Recent developments in optical sensor technology have the potential to enable direct detection of foliar disease under field conditions. This review assesses recent developments in the use of optical methods for detecting foliar disease, evaluates the likely benefits of spatially selective disease control in field crops, and discusses practicalities and limitations of using optical disease detection systems for crop protection in precision pest management.

Spectral Imaging II: Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps

Abstract: The objective of this research was to develop a ground-based real-time remote sensing system for detecting diseases in arable crops under field conditions and in an early stage of disease development, before it can visibly be detected. This was achieved through sensor fusion of hyper-spectral reflection information between 450 and 900nm and fluorescence imaging. The work reported here used yellow rust (Puccinia striiformis) disease of winter wheat as a model system for testing the featured technologies. Hyper-spectral reflection images of healthy and infected plants were taken with an imaging spectrograph under field circumstances and ambient lighting conditions. Multi-spectral fluorescence images were taken simultaneously on the same plants using UV-blue excitation. Through comparison of the 550 and 690nm fluorescence images, it was possible to detect disease presence. The fraction of pixels in one image, recognized as diseased, was set as the final fluorescence disease variable called the lesion index (LI). A spectral reflection method, based on only three wavebands, was developed that could discriminate disease from healthy with an overall error of about 11.3%. The method based on fluorescence was less accurate with an overall discrimination error of about 16.5%. However, fusing the measurements from the two approaches together allowed overall disease from healthy discrimination of 94.5% by using QDA. Data fusion was also performed using a Self-Organizing Map (SOM) neural network which decreased the overall classification error to 1%. The possible implementation of the SOM-based disease classifier for rapid retraining in the field is discussed. Further, the real-time aspects of the acquisition and processing of spectral and fluorescence images are discussed. With the proposed adaptations the multi-sensor fusion disease detection system can be applied in the real-time detection of plant disease in the field.

Machine Learning in Agriculture: A Review.

Abstract: Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Hyperspectral imaging – an emerging process analytical tool for food quality and safety control

Abstract: Hyperspectral imaging (HSI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This paper provides an introduction to hyperspectral imaging: HSI equipment, image acquisition and processing are described; current limitations and likely future applications are discussed. In addition, recent advances in the application of HSI to food safety and quality assessment are reviewed, such as contaminant detection, defect identification, constituent analysis and quality evaluation.