Abstract:
Nutrient deficiency in Citrus reticulata (mandarin orange) plants causes reduced plant resistance against diseases
and pests. This research presents a combined approach to identify nutrient deficiencies in Citrus Reticulata var. Fremont leaves
using image processing and machine learning techniques. This study uses leaf images to accurately and efficiently detect
nutrient deficiencies in mandarin orange plants. The image data is divided into four classes: normal, N-minus, P-minus, and
K-minus. The file sizes are compressed using a lossless compression method, resulting in an average file size reduction of
96.99%. Subsequently, the images undergo contrast stretching to improve their quality. Parameters such as Peak Signal to
Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are measured. The maximum PSNR is
35.801412680386456, and the minimum PSNR is 14.011790825259139, with a good range of 25-30 dB for PSNR. The SSIM
scores after compression and contrast stretching are 0.9734938845160109 (maximum) and 0.8860099106663607 (minimum),
which fall within the good range of 0.8-0.9. The second stage involves applying segmentation processes to the images using the
Canny and Sauvola methods. Canny effectively identifies sharp and clear edges, while Sauvola retains image details, making it
more suitable for texture and colour feature extraction. The third stage involves extracting colour and texture features from
the images. Colour feature extraction is done using the H (Hue), S (Saturation), and V (Value) colour space. Texture feature
extraction utilises the Grey-Level Co-Occurrence Matrix (GLCM) method. The feature values will be used for the classification
process in the next stage. The fourth stage involves the classification process based on the segmentation results using the Canny
and Sauvola methods, performed separately using Artificial Neural Network (ANN) and Support Vector Machine (SVM)
methods. These process results in four datasets: Canny-ANN, Canny-SVM, Sauvola-ANN, and Sauvola-SVM. The highest
accuracy is achieved by the Sauvola-ANN method, with a value of 93.75%.
