Identification of primary nutrient element deficiency in soybean based on leaf features
2016
Fauzi, A. | I GedeSusrama MD | Sukendah | Soedarto, T. | Idhom, M.
One of the problems in the cultivation of soybean is a deficiency of nutrient elements. Deficiency can be identified from the appearance of symptoms on the surface of the leaves. However, accurate identification requires expertise of some analysis. This study tried to create a quick method to identify nutrient element deficiency based on color features on the surface of the leaves. Deficiency of the major macro nutrients nitrogen, phosphorus, and potassium were identified using Classification Method of K-Nearest Neighbour (K-NN) and Artificial Neural Networks (ANN) Backpropagation. Since soybean leaf are usually green, the features that can be used is green color from RGB color's section (Red, Green, Blue). Characteristics used in the study were: average intensity, smoothness, entropy, five of moment invariant, energy, and contrast. Identification and classification were conducted on three primary nutrient elements, i.e. potassium, nitrogen and phosphorous using 30 samples for each nutrient. The results showed that the identification of nutrient element deficiency can be done by Classification. Method of K-Nearest Neighbour (K-NN) and Artificial Neural Networks (ANN) Backpropagation. The accuracy of K-Nearest Neighbour was 69.44% and 77.78% for Arfticial Neural Networks Backpropagation.
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