Image Processing Techniques for Harumanis Disease Severity and Weighting Estimation for Automatic Grading System Application

Harumanis Mango is known as the king of Mangoes. It is very nutritious and rich with carotenes. However, many of the farmers and agriculture experts reported that they have problems in grading and inspecting the Harumanis Mango. Sometimes, Mango production loses its quality due to diseases that are not even visible to the naked eyes. Traditionally, farmers and agriculture experts will estimate the severity of the disease using their experiences. While for weight estimation, manual inspection was done by using a weight scale. This traditional method has its own drawbacks as it can lead to some errors due to inconsistencies made by human inspection. Furthermore, they are less efficient and very time-consuming. Therefore, an automated procedure that able to classify the disease severities and weight estimations would be much appreciated. With the aid of image processing techniques, diseases can be classified according to its scale, and its weight can be estimated. A number of pixels of Harumanis Mango will be used for classification. The analysis will be done by using the statistical method of regression. It shows that the accuracy of weight estimation is 72.25%

Detection of Colletotrichum Gloeosporioides Fungus Isolates Development/Spread for Mango (Mangifera Indica L.) Cultivar from Electronic Nose Using Multivariate-Statistical Analysis

Agriculture plays a very important role in Asia economic sectors. For Malaysia, it plays a big contribution towards the country’s development. Mangifera Indica L., commonly known as Mango, is one of the fruit that has high economic demand and potential in Malaysia export business. However, due to radical climate changes from hot to humid, Mango is exposed towards a number of disease and this will affect its production. Colletotrichum gloeosporioides is one of the major diseases that could occur on any types of Mango. This fungus can attack on fruit skin and leaf, therefore a method that able to detect and control it would be much appreciated. Hence, this paper shows that the presence of Colletotrichum gloeosporioides type of pathogen can be detected by using Electronic Nose (E-Nose). The E-Nose will detect the Volatile Organic Compound (VOC) that produced from this fungus. Further analysis and justification on its existence are completed by using one of Multivariate-Statistical Analysis method which is Principal Component Analysis (PCA).The analysis results effectively show that the PCA is able to classify the number of isolating days of this type of fungus after cultured. Furthermore the potential of pre-symptomatic detection of the plant diseases was demonstrated