In Field Proximal Soil Sensing For Real Time Crop Recommendation Using Fuzzy Logic Model
The Philippines Center for Post-harvest Development and Mechanization (PhilMech) cited that most of Filipino farmers still rely on manual land cultivation which includes land preparation, planting and harvesting. With this, they are driven to engage in Precision Agriculture. It is an emerging agricultural technique based on a range of technologies like sensing devices and software applications. This research was therefore done to address the challenges of manual land cultivation and technologies of Precision Agriculture. Present technologies include detection of plant or soil nutrients and diseases but neither of these studies recommended crops to be planted considering the state of the soil. This study involved the creation of a stand-alone crop recommending device that detected soil quality and provided a list of selected general crops placed on a database. The device used different sensors that measured pH level, soil moisture, soil temperature and soil fertility. After designing the system for pH, soil fertility, soil moisture and temperature sensor, the next step was to build a fuzzy logic model for crop recommendation. The dataset used for building the membership functions for the fuzzy logic model was based on the Philippine Council for Agriculture and Resources Research Foundation Crops Recommendation database. The database was composed of recommended minimum and maximum values of temperature, pH, soil moisture, and soil fertility recommended for growing each of the 30 crops commonly planted in the country. From there, the fuzzy rules were established to classify whether a crop is bad, okay or good to be planted on the given soil sample. The device increased the precision of crop cultivation and consequently increased profit to the farmers as they utilize their resources effectively. For future improvements of this study, the actual ambient condition and more advanced sensing technologies can be considered.