Design of Brassica Chinensis L. Nutrient Deficiency Detection and Fertilizing System based on Deep Convolutional Neural Network

Abstract

Plant detection is one of the applications of image processing in agriculture that is being linked with various agricultural tasks nowadays - such as the ability to detect nutritional deficiencies in plants based on the appearance of their leaves. This study is an attempt to contribute on the detection of nutrient deficiency through image processing of Brassica Chinensis L., which usually displays symptoms based on NPK nutrient deficiency and is also locally known as pechay. The study is centered on the hardware development that will be mounted on a robotic prototype and be able to assess a pechay’s health and fertilize it if it lacks nutrients. The detection was based on DenseNet121 model which was trained over numerous healthy and deficient pechay images. If the detection part determines that the pechay was healthy, then the fertilizing system will not release fertilizers. But when it determines the plant to be deficient, the fertilizing system will sprinkle fertilizers on the pechay. The overall system was able to achieve an 85% accuracy in an actual farm set-up. The system was further validated by comparing its results versus the visual inspection results of real-life farmer, and it was found out to be still accurate at 86%. The system further assists the farm owners in reducing the expenses of fertilizer usage and any dangers associated with growing a crop of non-deficient pechay plants. Overall, the process of automated fertilizer system was made possible by using nutrient deficiency detection as the decision-making process.