scholarly journals Purwarupa Sistem Prediksi Luas dan Hasil Panen Padi suatu Wilayah menggunakan Pengolahan Citra Digital dengan Metode Sobel dan Otsu

Author(s):  
Ardya Yunita Putri ◽  
Raden Sumiharto

Area and paddy crop yield prediction system of an area using  image processing by Sobel  Otsu’s method is one of  system that utilize aerial photo data for measuring  area and prediction of its crop yield. Otsu’s method is used to thresholding process and  Sobel’s method is used to detect paddy field’s edges that will calculate its area. Then filtering process so that the scanning process white pixels are counted only exist in the desired region. After the amount of white pixel(s) is obtained, their amount is multiplied with the scale that obtained from calibration process and crop yield prediction (kg/m2). Detection of yellow paddy color that ready-to-harvest is successfully performed by processing the HSV color, which is then detected by thresholding HSV. At the time of testing with variety of paddy color, the detected paddy color is the paddy color ready-to-harvest, which is brownish yellow that represented by white pixels, and will be used then to predict its area and crop yield. Thereafter, accuracy calculation test resulting in different error levels in different paddy fields. Error in testing of this system are 3,1 %, 8,7%, 4,9% dan 248%. The highest error value is caused by excessive exposure of light, with the result that the green color on paddy is detected by the system as yellow and some areas are covered by trees that, thereby reducing paddy fields area calculation.

In agriculture image processing and Datamining play an important role. Prediction of crop yield prediction is very important in tea production. Image segmentation is used to segment the disease affected region in the leaf. It segment image into various homogeneous region. In this paper color and Region’s mean based segmentation technique is introduced to subtract background and fore ground. This new approach is analyzed and compared based on five performance metrics such as PSNR (Peak Signal to Noise Ratio) Value, Rand Index (RI), precision, recall and accuracy. The proposed method gave better accuracy than other methods.


Author(s):  
Sujata Mulik

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 


Author(s):  
Fatin Farhan Haque ◽  
Ahmed Abdelgawad ◽  
Venkata Prasanth Yanambaka ◽  
Kumar Yelamarthi

Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant

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