2 Remote-sensing of Plant Diseases

Transcript

Hello! Welcome to the second talk of this week that is Remote sensing of plant diseases. In the previous talk we discussed about the Proximal sensing of plant diseases and in today’s talk we will be talking on Remote sensing of plant diseases, that means tools and devices used to sense plat that is being suffered by a particular disease form a very distant place without coming directly in contact with the plant canopy or plant tissues.

So, Remote Sensing is method used to obtain information from plants or crops without direct contact or invasive manipulation. To obtain information on an object by measuring the electromagnetic energy reflected/backscattered or emitted by the surface of the earth. The measurements are possessed and analyzed to retrieve information on the object observed for example plant health in this case. SO, remote sensing is an indirect assessment technique which is able to monitor vegetation conditions from distance, and evaluate the spatial extent and patterns of vegetation characteristics and plant health, in this application. So a plant which is in stressed conditions that is (induced by a disease) reacts with potassium mechanisms that lead to suboptimal growth which show up as changes in variables such as leaf area index (LAI), chlorophyll content, or surface temperature and thus producing a spectral signature different from the signature of healthy or unstressed vegetation.

So, this is the basic principle how remote sensing works. The spectrum that is produced by a healthy plant and an infected plant based on the lot of chlorophyll or leaf area index or the surface temperature in general so at certain times it is not possible for even a human eyes to go beyond a particular area to see the extent of damage that has been taking place in large scale cultivations.

When plants are exposed to pathogens they activate defense responses whose molecular mechanisms are very complex. At the early stages, when visual symptoms such as lesions on the leaf surface are not present, plants react to those presences of a pathogen with physiological mechanism such as reduction of the photosynthesis rate, which induces an increase of fluorescence and heat emission from the infected plant leaves or plant tissues. The presence of stress factors changes the thermal properties of plants, which in term influences the radiation emitted in the TIR domain of the spectrum, mainly produced by changes of the water content of leaves which can also be detected at the early stages of the disease. So, it is the physiological condition of the plant when an early infection takes place that is having a different spectrum signature and this different signature spectrum can be detected through this remote sensing devices and it can very well diagnose whether the plant is being infected by a particular pathogen or not. The Remote Sensing community defines plant disease monitoring as: detection that is (deviation from the healthy), identification that is (diagnosis of specific symptoms among others and differentiation of previous diseases), and quantification that is measurement of disease severity that is percent leaf area affected and so on. Different sensors and techniques are required for detecting plant response to various diseases and disease severity.

Here is an example of Field reflectance spectra of healthy tomato plants an infected with late blight disease. Here you can see the spectrum, top one is the healthy spectrums where as spectrums below are depending on the disease severity and the last spectrum is mostly from the severely infected plant tissues. So with this type of spectral analysis one can establish that how much disease severity is being there because healthy being at the top and severity level is at this level so the gap can be calculated and accordingly severity of the disease can be established. So, this is another example of spectral reflectance of healthy wheat as well as wheat infected by rust pathogen. Here, also in terms of disease severity this is the healthy one this is germinated from severely infected plants. So, this spectral analysis can give us the severity level how severe is the disease.

This is a real image from wheat field where wheat crops are suffering from yellow rust in the field and with time one can see that the progression of spectrum is getting changed where the red ones are the very severely infected areas of the wheat lot whereas, moderate yellow signifies the moderately affected areas whereas, orange is the serious infected areas and green remains the healthy areas of the plant. So, from a long distance one can see that whether the field is getting affected and if it is getting affected by a particular disease how severe the disease problem is looking into the spectral pattern.

Similarly, there is another trail that is conducted with Sheath Blight of rice where 67 rice cultivators were used in a research plot and the data was collected for the remote sensing device to see what is the progression and how many varieties are getting affected by Sheath Blight (ShB) and what is the severity level of the disease in those varieties in a single experiment.

So this is the typical output image of the Sheath Blight detection by remote sensing devices where it was shown that, this is the normal RGB light play and this the HLS (high resolution spectrums) for the same plots and this is the first observation and this is the second observation. So, in the second observation certain cultivars’ were severely affected by the sheath blight pathogen which can be very clearly distinguished from the spectral data that originates from the remote sensing devices. So with this even one can look at the differences in the varieties that is responding to a particular disease like sheath blight in this particular case.

So the remote sensing device can be helpful to collect data and to get the large scale information from field such as blight in case of rice cultivars with like sheath blight pathogens.

So with this we have seen the remote sensing devices, how it works and how it can be used for collecting data at large scale and getting appropriate information very accurately and correctly at a very short time interval. So this is also a technology that can be used for other agricultural purposes for measurement of abiotic stresses or pest infestations but it is widely being used in diagnosis and detection of plant pathogens causing plant diseases.

So with this we have to an end of today’s talk and in the next talk will be talking about interaction of human pathogens on plants and how they can be identified from plants and how appropriate measures can be taken after identification of human pathogens on plants. Till then have a good time.

Thank You very much.