Defects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction

Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.

Climate variables effect on fruiting pattern of Kinnow mandarin (Citrus nobilis Lour × C. deliciosa Tenora) grown at different agro-climatic regions

Kinnow orchards grown in different agro-ecological regions of Punjab, Pakistan, namely Sargodha, Toba Tek Singh (TTS) and Vehari districts, were selected to assess the effect of climate variables on fruit-bearing patterns. Experiment was laid out in RCBD while selecting identical features Kinnow plants and labeled twigs at analogous canopy positions in all three sites. Temperature was reported higher in TTS and Vehari areas, while relative humidity in Sargodha accounted for different levels of agrometeorological indices by computing more variations in warm districts. Climate variables influenced fruit-bearing habits and vegetative growth trend in all three flushes while recording heavy fruit-bearing plants during on-year and light fruit-bearing in off-year at Vehari. Similarly, three vegetative flushes were recorded unevenly in all three sites due to different fruit-bearing patterns induced by climate variables. Harvesting pattern of orchards began earlier in Sargodha, where maximum orchards were harvested before new flowering to add evenness to fruiting habits during on & off-years. In warm conditions, fruit ripening arrived in the peak of winter and mostly domestic market-driven harvesting resulted in late start of fruit picking with more erratic fruit-bearing habits. Both physiological and pathological fruit drops have been significantly affected by climate variables with a higher degree of physiological drop in warm regions and pathological effects in the humid conditions of Sargodha on heavy fruit-bearing plants. Fruit yield and grading quality were also affected in both seasons by showing more asymmetrical trend in yield and fruit grading in warm areas of TTS and Vehari due to an irregular fruiting pattern compared to Sargodha. From now on, the climate variables of the three sites directly influenced the fruiting patterns, vegetative flushes, fruit drops, yields and grades of Kinnow mandarin.

Quality Identification: Fruit Grading System

In India, agriculture plays a major role in the economic development. Agriculture must be able to produce fruit of better quality and grow at a faster rate. With the use of different image processing techniques, improvement in agriculture field for quality identification, sorting the fruits with different quality, irrigation becomes feasible. Major parameters considered are reduction in the time required and cost efficient, using Image processing. In this proposal, we have provided a technique to address the challenge of fruit grading using image processing techniques for smart farming.

Automated Fruit Grading System Using Image Fusion

This work proposed a recognition system capable of identifying an Indian fruit from among a set, established in a database, using computer vision techniques. The investigation made it possible to compare the image color models, together with the size and shape characteristics previously used by different researcher. For the class of fruits defined in this investigation, it was determined that the characteristics that best described them were the average values of the RGB channels and the length of the major and minor axes when the image fusion technique is used, a process that allowed obtaining results with an accuracy equal to 92% in the tests carried out, finding that not always selecting a greater number of variables to form the descriptor vector allows the classifiers to deliver a more accurate response. In this sense it is important to consider that among the study variables a low dependency or correlation value.