Artificial Intelligence applied to agriculture: detection of types and progress of diseases in vineyards

Chile’s wine industry has experienced sustained growth over the last few decades, becoming one of the main players in the global wine trade, with exports to over 109 countries across 5 continents.

At the country level, both grape and wine production is concentrated in two regions: the Maule region and the O’Higgins region, with annual growth rates of 3.15% and 7.40% respectively. However, there is a great threat to this growth associated with the exponential development of diseases in vineyards that can lead to lower production and loss of competitiveness in the market. 

At world level, the United Nations Food and Agriculture Organisation estimates that annual losses due to pests are between 20% and 40% of global production, which translates into around 232 billion dollars per year. Along the same lines, the Fundación para la Innovación Agraria de Chile, in its report Detección de virus y fitoplasmas en vid, points out that:

  • It has been observed that the diseases produced by these agents produce a reduction in production of around 23%. This has as a direct consequence a very significant drop in profitability, incurring a reduction in the net margin that would lead to an unbearable deficit for the producer. This situation is particularly compromised in the O’Higgins region, since according to data from the Oficina de Estudios y Políticas Agrarias (ODEPA), more than 76% of vineyard holdings are on land of less than 20 hectares, which could mean, in general, a lower financial capacity to face such significant economic losses.
  • Over the last few decades, much of the area covered by vines has been replaced by new varieties using the same soils and infrastructure, practices that have led to an increase in populations of nematodes, insects, fungi and bacteria.

To face this threat, the best approach is to be able to carry out an early, rapid and reliable detection of diseases that may affect vineyards, as this will enable early corrective measures to be designed and adopted consisting of the rapid isolation and removal of affected specimens, thus preventing these diseases from spreading throughout the vines and causing significant losses associated with production. 

The limitation in adopting this approach is that there is currently no technology available on the market for diagnosing the health condition of vineyards with high precision, at a low cost and in real time.  In fact, wine producers currently rely on visual inspection and expert assessment to control the development of these diseases, as the alternatives are expensive and not always available, as well as being techniques with low reliability.

Our solution to the problem: disease detection through the processing of multispectral images acquired by a drone.

As we have already indicated, most wine producers carry out the inspection task by visual observation and expert judgement.  This form of inspection consists of carrying out a general observation of the vineyards, trying to visualise, based on empirically acquired subjective criteria, whether the plant has any symptoms that indicate its disease. Doing it in this way involves a great amount of time and resources since direct observation depends on the visual skills of the expert in vineyard diseases who carries it out and his subjective assessment at that moment (a single inspector may take hours to inspect only one hectare). In turn, this method has a relatively high margin of error, since the detection of symptoms or anomalies in plantations depends on the subjectivity of the inspectors, the height and size of the vine, or the climatic condition, among others.

Other non-intrusive techniques available for the detection of diseases are those predictive models that study the evolution of fungi in the grapevine in order to predict infections. These models use meteorological variables such as temperature, rainfall and relative humidity. For example, in the specific case of Mildew (Plasmopara viticola fungus) – a disease well known by winegrowers due to the serious damage it causes to the vineyard – the proposed system is based on the Goidanich model. This model collects meteorological information (average temperature and relative humidity) and estimates when the asexual fruiting of the fungus will occur, which is the ideal time to treat the vine with products to prevent the germination of zoospores. The major limitation of this technique is a very high relative error

On the other hand, as regards intrusive techniques for detecting diseases in vineyards, the technological tools developed are based on the combined use of molecular techniques for detecting viruses (ELISA and RT-PCR) and cytoplasms (nested-PCR) in wine and table grapes. This consists of field sampling of the vegetations to be analysed and their subsequent processing and laboratory analysis. It should be noted that the ELISA (Enzyme Linked Immune Sorbent Assay) technique is used as a support (in the case of virus detection) for the PCR (Polymerase Chain Reaction) gene technique, which is suitable for the detection of both viruses and cytoplasms. The limitation of this way of analysing the infection of the vineyards is its very high cost and time consuming.

Based on this reality, together with the wine producers themselves, we identified the need to develop a disruptive solution capable of overcoming the technical and economic limitations of these existing vineyard inspection practices both nationally and internationally.

Our methodology for developing the solution

Through our methodology, starting from the requirements defined as a result of the analysis and research carried out, we were able to define the best approach to solve the problem, specifying technical performance and carrying out a cost-benefit analysis. Thus, we proposed which should be the requirements, which we list below: 

  • Measurement of large areas rapidly and efficiently.
  • Discretization of the cultivation area into individual elements and obtaining relevant parameters in an automated way.
  • Classification of the discretization into “affected” and “not affected”, also indicating type of disease and progress.
  • Highly accurate information within 24-48 hours and at a low cost, enabling decisions to be taken at early stages.
  • Non-intrusive system, with no interference with the crops. 

Based on this approach, and after an exhaustive analysis of the state of the art, we determined that the best approach was to develop a comprehensive system for monitoring the health condition of vineyards (disease detection) using spectral images captured by unmanned aircraft (drones).

Diagram of our solution for diagnosing the health condition of vineyards

The concept behind this innovative solution is to investigate physiological disturbances by registering changes in leaf reflectance in the near-infrared part of the spectrum, which is not visible to the human eye. Using this approach we can analyse biotic and abiotic stress from differences in the spectral characteristics of the crop canopy. 

In order to identify those spectral parameters that are most characteristic for the detection of diseases in vines, we carried out an analysis of different scientific articles that allowed us to notice that one of the most used indexes is the NDVI (Normalized Difference Vegetation Index). This is a good parameter to evaluate the chlorophyll content of the leaf, which is directly related to the health condition of the plants. Furthermore, from the information collected, it has been possible to implement 8 spectral indexes that can be calculated on each individual tree.

Therefore, returning to the configuration and operation of the solution, the acquisition system (drone) performs a flight over the ground at an altitude of approximately 30 metres using an unmanned aerial vehicle equipped with a five-band multispectral camera.

Tests for validation of the drone equipped with the multispectral camera

After the flight of the drone equipped with the multispectral camera, the data is sent to a cloud server. Using the spectral images, an orthophoto is generated and an atmospheric correction is made. After this, the trees are identified and the spectral indexes of each unit are calculated, with a neural network being used to assign the health condition. 

Therefore, the treatment of the images captured in the drone flight is carried out through a specific algorithm developed to be executed in different steps. 

  • The first step consists of processing and correcting the images obtained, since this data must be prepared in such a way as to eliminate the disturbance caused by the spatial variations that occur in the information acquisition process, and by the need to adjust the spatial information to a given reference system. Thus, the ortho-rectification of the multispectral images is achieved and the atmospheric correction allows the calibration of the radiation values to reflectance using the total incoming irradiance.

Example of orthophoto image

  • Once this was achieved, the next challenge was to identify and count the different trees by analysing high definition images taken from a drone, to identify each individual tree, obtaining a geolocated map of them and counting the quantity in a given area.

Results of detection and counting algorithms

  • As a third step, representative variables were selected and the health condition of the vineyards was classified according to the diseases to be identified. To do this, the indexes and parameters that may represent health condition were taken into account, such as NDVI (Normalized Difference Vegetation Index). 

Image with the 8 spectral indexes on the trees


For this purpose, a classification algorithm was developed, based on multi-layer perceptual neural network that are trained using the maximum likelihood training method, which will select the most representative parameters of each disease, as well as a weighting coefficient, based on successive trainings carried out in the field. 

After obtaining the diagnosis of diseases through the described processing, it is delivered to the client through a digital platform so that the vineyards can be managed. This platform allows the health condition of the analysed plantations to be visualised and positioned on the map, providing geometric characteristics, a map of spectral indexes, identification of the health condition of each vine, filtered on the map by diseases and general statistics and historical data.

In addition, within 48 hours, the client receives a report of the flight made, a document in electronic format with the results of the flight, which includes discretization of the vines, information and geolocalised location of the vines affected by any disease and the progress of the same together with a possible plan of action depending on the disease detected.


Our team has developed an integrated software and hardware system that allows to diagnose the health condition of the vineyard crops in real time, in an accurate way, and at low cost, resulting in a great help to the producers who want to reduce the risk of spreading diseases and the consequent economic losses.

Thanks to the application of Artificial Intelligence, the analysis of spectral images captured by drones are processed by means of complex algorithms that include neural networks, in order to develop a system for diagnosing the health condition of vineyards capable of detecting whether the vine plants analysed suffer from any type of disease, what type of disease affects them and their progress.

For more information on this innovative solution, please download our specific brochure about it. 

In addition, if you are looking to improve the productivity of your vineyard, do not hesitate to consult our other solutions for this industrial sector or contact us to jointly develop a tailor-made technological solution that responds to your specific needs. 


Carolina Buzzetti Horta (2018). Una mirada al mercado vitivinícola nacional e internacional.

Fundación para la Innovación Agraria de Chile (2015). Experiencias de innovación para el emprendimiento agrario. Resultados y lecciones en detección de virus y fitoplasmas en vid: proyecto de innovación entre IV región de Coquimbo y VII región del Maule.

Martín, M. A., & Mimbrero, M. R. (2015). Desarrollo e implementación de un sistema para detección temprana de enfermedades en vid en entorno R.

Fundación para la Innovación Agraria Chile (2009). Experiencias de innovación para el emprendimiento agrario. Resultados y lecciones en detección de virus y fitoplasmas en vid: proyecto de innovación entre IV región de Coquimbo y VII región del Maule., 31.

Share on twitter
Share on linkedin
Share on whatsapp
Share on telegram
Share on email
Scroll to Top
Utilizamos cookies propias y de terceros para mejorar la experiencia del usuario a través de su navegación. Si continúas navegando aceptas su uso.    Más información