Feature aggregation for nutrient deficiency identification in chili based on machine learning

Macronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive
methods that plays a role in processing plant image data based on specific characteristics is computer vision.
This study uses 5166 image data after augmentation process for six plant health conditions. But the analysis of
one feature cannot represent plant health condition. Therefore, a careful combination of features is required.
This study combines three types of features with HSV and RGB for color, GLCM and LBP for texture, and Hu moments
and centroid distance for shapes. Each feature and its combination are trained and tested using the same
MLP architecture. The combination of RGB, GLCM, Humoments, and Distance of centroid features results the best
performance. In addition, this study compares theMLP architecture usedwith previous studies such as SVM, Random
Forest Technique, Naive Bayes, and CNN. CNN produced the best performance, followed by SVM and MLP,
with accuracy reaching 97.76%, 90.55% and 89.70%, respectively. Although MLP has lower accuracy than CNN,
themodel for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural
environment.