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\section{Abstract}  Cacao is a high valued crop that can be processed into products such as chocolate, liquor, and lotion. ThePhilippines is one of the producers of cacao that supplies the world’s increasing demand of cacao products. However, the  Philippine Bureau of Agricultural Research reported that farms lose 20\% to 30\% of the cacao pods, and up to 10\% of trees annually due to various cacao pests and diseases. To reduce the damage, farmers and agricultural technicians regularly monitor the well-being of their crops. But at present they rely on visual inspection to assess the degree of infestation of their crops, resulting to several errors and inconsistencies due to the subjective nature of the assessment procedure. To improve the inspection procedure, this research provides an initial step towards developing a tool that measures the level of infestation of the cacao pods through image processing techniques. This tool is intended to provide a quick, accurate, and objective evaluation of cacao pods requiring minimal human interaction. A database consisting of \textit{(provide number)} web-searched images of cacao pod diseases were obtained and analyzed. Exploratory findings showed that features extracted from the RGB color space provided the most discrimination between infected and non-infected cacao pod parts, particularly the intensity of the "greenness" of the pixel. Then, \textit{(comparison between SVM and ANN )}