Soil is known to be the most valuable natural source for all agriculture fields. Soil has two properties, namely-physical and chemical. These properties include soil moisture, texture, etc. and the latter include pH value. Soil texture plays an important role in crop cultivation. The physical properties of soil such as texture and granular size determine the water and nutrient holding capacity. Also the chemical property like pH value is very important for plant growth and development. Soil having pH value between 5.5 and 7 is optimal for agricultural purpose. Hence, a detailed study of soil pH property is necessary for cultivation. But laboratory method of soil pH calculation is a very costly and tedious process. Therefore, it is essential to develop an expert-based system that will overcome this issue. However, the system must be able to give correct result and should match with those conducted in laboratory. Farmers analyze pH either in lab or by soil pH card based on soil image color. But this is not an effective method since it relies heavily on human perception. Hence, we have developed an expert based system which can determine the pH of the soil without any human error. For this, we have conducted our experiments with the help of MatLab tool and smart phone as we have concerned about the rural farmers. We have analyzed and compared the proposed system results with the traditional laboratory methods with regression and have found 86 % accuracy in our model.
Automatic detection of citrus leaves disease is very much essential for the better productivity of citrus. Citrus leaves are affected by bacteria, fungus and virus respectively. Farmer detects the diseases of the plant using laboratory, naked eyes or using expert’s view. The rural farmers often face difficulties to detect these diseases due to the non availability of the laboratories in their area. Here in this paper, a computer automation system is proposed to detect the diseases of citrus leaves on an early stage. Citrus leaves images are captured using Smartphone. Captured images are used to extract the different features of the citrus leaves samples using Gray Level Co-occurrence Matrix. Finally, citrus greening and citrus CTV images are classified from citrus healthy images using Gaussian kernel based support vector machine. Accuracy of the kernel is evaluated for the different values of Gamma parameter of kernel. The Gaussian kernel gives maximum accuracy (95.5%) with Gamma value 1.
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