Smart Farming and Human Bioinformatics System Based on Context-Aware Computing Systems

Fertilizer is an important product that contributes to the growth of crops. As soil nutrients decrease, exotic and special fertilizers such as nitrogen, phosphorus, potassium, calcium, magnesium, soy milk, and sulfur are replenished in the soil. Anyhow the use of chemical fertilizers affects the lifestyle of farmers as well as the health of their crops. This chapter addresses the health of farmers and the health of crops by analyzing the characteristics of soil, environmental characteristics, and characteristics of healthy farmers. The main health problems of farmers include skin problems, lung problems, heart diseases, and cancer. This advice can suggest to farmers the best fertilizer to use to increase future crops. Hadoop Distributed File System (HDFS) has four levels of processing, like data polishing, extraction of features and matching similarity, binary analysis, and data clustering. The first stage cleans the data, removes missing values, and then performs data normalization and component decomposition. In the second stage, soil, environmental, and farmer health characteristics are extracted. The similarity is then evaluated based on the construction of ontology-supported grid reduction (OMR) to predict farmers’ health problems. In the third stage, the FP-growth algorithm and densely connected recurrent neural network (DC-RNN) are used to classify healthy farmers and healthy crops. In the fourth stage, the last group of farmers is presented with product health information from the self-planning map and, accordingly, product and fertilizer recommendations that will reduce health risks. Recommendations were made by HDFS, and performance was evaluated concerning parameters like precision, recall, F measurement, and accuracy on papaya, banana, and leafy vegetables.