Knowledge based Soil Classification Towards Relevant Crop Production

Abstract

Pakistan’s economy is strongly associated with agriculture sector. For a country having 25 % of GDP contributed through agriculture, there is a need to modernize the agriculture by acclimatizing contemporary approaches. Unfortunately, it has become a common trend among farmers to cultivate crops, being used in food items or which can easily be sold out in the market without using knowledge about the suitability or relevancy of crops to the soil environment. Consequently, the farmers face financial losses. Many researchers have proposed soil classification methods for various soils related researches, but they have very little contribution towards guidance of the farmers to select most suitable crops for cultivation at a particular soil type. Without the use of technology and computer-assisted approaches, the process of classifying soil environments could not help the farmers in taking decisions regarding appropriate crop selection in their respective fields. In this paper, an effective knowledge-oriented approach for soil classification in Pakistan has been presented using crowd sourced data obtained from 1557 users regarding 103 agricultural zones. The data were also obtained from AIMS (Govt. of Punjab) and Ministry of National Food Security & Research. In this work, random forest classifier has been used for processing and predicting complex tiered relationship among soil types belonging to agricultural zones and major suitable crops for improving yield production. The proposed model helps in computing the degree of relevancy of crop to agricultural region that help former selecting suitable crops for their cultivated lands.