Plant diseases are generally caused by pest, insects, pathogens and decrease the productivity to large scale if not controlled within time. Agriculturists are facing lose due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge that is in acres. The proposed system provides the solution for regularly monitoring the cultivated area and provides the automated disease detection using remote sensing images. The proposed system intimates the agriculturist about the crop diseases to take further actions. The objective of the proposed system is to early detection of diseases as soon as it starts spreading on the outer layer of the leaves. The proposed system works in two phases: the first phase deals with training data sets. This includes, training both healthy and as well as diseased data sets. The second phase deals with monitoring the crop and identifying the disease using Canny’s edge detection algorithm.
Agriculture gave birth to civilization. India is an agrarian country and its economy largely based upon crop production. Agriculture is the backbone of every economy. In a country like India which has ever increasing demand of food due to the raising population, advances in agriculture sector are required to meet the need.
The agriculture sector needs a huge up-gradation in order to survive the changing conditions of Indian economy. For optimum yield, the crop should be healthy, therefore some highly technical method is needed for periodic monitoring of crop. Crop disease is one of the major factor which indirectly influence the significant reduction of both quality and quantity of agricultural products.
A number of varieties of pesticides are available to control diseases and increase the production. But finding the most current disease, appropriate and effective pesticide to control the infected disease is difficult and requires experts advise which is time consuming and expensive.
The proposed system has two phases, first phase deals with training datasets. Both healthy and diseased leaf images are collected. Once the dataset is ready with healthy and infected image samples, the threshold is extracted for both aging and for diseases.
Periodically images are obtained by remote sensing. RGB values of the monitored images are extracted and compared with threshold images. If the threshold is greater or less than given value, histogram analysis and edge detection techniques are used to identify particular plant diseases.
Different types of crops given as the input to the Training Model. For each crop, plenty of Healthy and defected crop images are considered. Set the threshold value for each crop. Train the model in a such a way that it should take the proper decision for all types of crops.
Different spatial resolution images are obtained from different agricultural satellites such as NASA’s TERRA satellite, RESAT-1,PSLV-C16 and PSLV-C36.
Reference image is given as input to DSS. This system makes use of trained model to take the further decision. Trained model uses the dataset to provide a proper suggestion. If there is a huge change in the threshold of the Reference Image, then check whether its due to aging. If so, no need of intimating to the farmer. But if the change is due to disease that is deformities in leaves and color change in leaves will be identified then edge detection and histogram analysis will be carried out to give the proper solution.
The Process of Canny edge detection algorithm can be broken down to 5 different steps:
1. Apply Gaussian filter to smooth the image in order to remove the noise
2. Find the intensity gradients of the image
3. Apply non-maximum suppression to get rid of spurious response to edge detection
4. Apply double threshold to determine potential edges
5. Track edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges.
Algorithm for Disease Detection
1. Start
2. Read the reference image
3. Check the threshold
4. If change in threshold due to aging then goto step 7.
5. Else a. Convert the image into grayscale. b. Apply Canny edge detection algorithm c. Get the histogram value.
6. Identify the particular disease for the reference image.
7. Stop
The proposed system periodically monitors the cultivated field. Crop diseases are detected in early stage by using edge detection and histogram matching. Machine learning techniques are used to train the model which helps to take a proper decision regarding the diseases. The pesticide as a remedy is suggested to the farmer for infected diseases to control it. In future the proposed system may be implemented by adding extra services like near by government stores, price list for the pesticides, near by open market and many more.
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