ABSTRACT
The development in science and technology has vital contribution towards improving the country's economy. One of the sectors that contribute to the country's economy is agriculture which needs the improvement of science and technology from time to time such as in its fertigation system. The manual applications of fertilizer that are commonly used are very stressful and consume enormous time especially when cultivating a large area of land and also do not ensure efficient management of fertilizer. Inefficient management of nutrient inputs has put a large constraint on the environment and human' s health. Indiscriminate use of nitrogen and phosphorus fertilizers has led to ground water pollution. So farmers have to pay close attention to nutrient management and incorporate the concept of balanced plant nutrition into their farming techniques. So in this project, I am presenting a decision support system for an automatic Fertigation of tomato plant using artificial neural network. The system was developed in a MATLAB environment using the MATLAB GUI toolbox and the MATLAB neural network toolbox. The system was designed in a way that user can input the image corresponding to the growth stage of the tomato plant and the system will be able to identify the name of the growth stage. The system is able to automatically state when to dispense fertilizer to tomato plant at different stages of its development using the input image for the growth stages, the selected nitrogen content, phosphorous content, and potassium content of the soil. For twelve test samples that were taken, an accuracy of 91.67% was achieved. Therefore, the decision of the system is said to be accurate.
