Growth Stage Identification for Cherry Tomato using Image Processing Techniques

Abstract

Controlled environment agriculture are being developed with the purpose of increasing production yield in farms. For optimal yield, it is very important to have an understanding about the relationship of environmental factors such as radiation, temperature, nutrients, water, and in relation with the growth state of the crop. Growth monitoring of cherry tomato crops in traditional methods are extremely labor-intensive, destructive, and costly in terms of time and money. Thus, application of computer vision has become an area of interest in the study of monitoring tomatoes' growth. In this study, image processing techniques are employed to identify the growth stage of cherry tomato as fruiting, flowering, and leafing stage. Confusion matrix with True Positive rate and False negative rate, and ROC are used to evaluate the decision support system developed. Experimental results show a high performance in determining the growth stage of test cherry tomato images.