SOIL MINERAL DEFICIENCY DETECTION USING A DEEP LEARNING ALGORITHM COMMONLY KNOWN AS CONVOLUTIONAL NEURAL NETWORKS

The Agricultural sector is one of the leading economic sectors in Africa providing employment to 60% of the population and about 70% are women (Women, Agriculture and Work in Africa, 2018) making it a primary source of food and income in their families. It is also estimated that agriculture contributes 15% of Africa’s Gross Domestic Product. However this contribution has kept on deteriorating in the last five years.Despite the agricultural growth and consequent improvement in Africa, plant yield from agriculture is still poor due to low nutrient content in the soil. Based on the results from the FAO food trials it shows that there has been a decline in the yield of crops due to low nitrogen and phosphorus content which are part of the fundamental nutrients. The yield deficit rose from 5% to 15% between 1975 and 2005 and this led to a rise of malnourished cases. According to FAO data for 2010, around 73 % of the people lived on less than two dollars per day, almost 28 % did not consume enough calories, and 24 % of the children under five were underweight. Of 925 million hungry people in the world, 239 million lived in sub-Saharan Africa. Interestingly as per 80% of arable land in Africa has low soil fertility and significant amounts of nutrients are lost every year due to unsustainable soil management practices.Since no efforts have been made to avert the nutrient crisis in Africa and farmers are still finding difficulty in identifying the nutrient content of their land, I propose SoMitLab a simple and novel solution that will enable farmers detect the mineral content majorly nitrogen, phosphorus and potassium since they are the fundamental nutrients needed by almost all plants to produce the best yields in their gardens. The results shall be in real time using smartphones and a sampling device attached.It shall be done in two phases i.e the soil sample extraction, preparation and image capture while the second phase is mainly image analysis and reporting.