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Grading of fruits based on their ripeness has been a topic of research for the last two decades. Identifying the ripened mangoes has become more of an art than science and is a challenging task. This study aims at introducing a system to grade mangoes with four classes based on their ripeness. The study was demonstrated through an extensive experimentation on a newly created dataset consisting of 981 images of Alphonso mango variety belonging to four classes viz., under-ripen, perfectly ripen, over-ripen with internal defects and over-ripen without internal defects. In this study, a hierarchical approach was adopted to classify the mangoes into the four classes. At each stage of classification, L*a*b color space features were extracted. For the purpose of classification at each stage, a number of classifiers and their possible combinations were tried out. The study revealed that, the Support Vector Machine (SVM) classifier works better for classifying mangoes into under-ripen, perfectly ripen and over-ripen while the thresholding classifier has a superior classification performance on over-ripen with internal defects and over-ripen without internal defects. Further, to bring out the superiority of the hierarchical approach, a conventional single shot multi-class classification approach with SVM was also studied. The results of the experimentation demonstrated that the hierarchical method with an accuracy of 88% outperforms the counterpart conventional single shot multi-class classification approach in addition to several existing contemporary models.

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN., 10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer Science, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015)

Cassava is a major food crop for farmers and especially small holder farmers and cultivated under low input other than the irrigation. It is cultivated as mono crop or intercrop at early stage and cultivating throughout the year. It is harvested carefully because of cyanogenic glucosides and consumed with in day. Easy and un-damageable uprooting of the tuber mainly depends on soil moisture, texture and agronomic practices. The study was focused with the objectives of the assessment of soil loss due to the harvesting of cassava roots tubers under low input agriculture, and to estimate the amount of plant nutrients loss due to crop harvest for cassava. Also the observation was made the correlation between the soil loss and physical characters of the tuber, soil texture and agronomic practices. Average plant specific soil loss due to crop harvesting was 80. 7 g root − 1 and crop specific soil loss due to crop harvesting was 7. 64 kg ha − 1 harvest − 1 loss in Valliagmam area in Jaffna, Sri Lanka. Soil moisture content at harvesting time was a significant factor that explained the variations in the soil lost at cassava harvesting. Soil moisture has linear positive relationship with average plant specific soil loss due to crop harvesting. Soil nutrient loss during cassava harvesting by removal of adhering soil with root tuber was 1. 15 kg of N, 1. 99 kg of P and 2. 91 kg of K ha − 1 harvest − 1 . Application of fertilizer is important since considerable amount nutrient loss was observed due to soil loss due to crop harvest.