This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.

The pollutants released from pharmaceutical, steel, paper, and battery industries into water cause stress on the natural ecosystems, may mix with soil and water, enter into human food chain, and hence cause irreparable damage to the biotic system. Hence, the appropriate monitoring of water along with determination of heavy metals is very important for human beings. In present paper, total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples gathered from the various sites of an identified industrial area. Experimentation is carried out at Raja Ramanna Centre for Advanced Technology (RRCAT), Indore‐India by using TXRF, which is one the advance techniques of element determination up to ppb levels. The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits established by the WHO (World Health Organization) and BIS (Bureau of Indian Standard) guidelines regarding drinking water use. The levels of the Pb, Fe, As, Ni, Cr, Co, and Mn exceeded the values suggested by WHO and BIS at some locations/sites. To better understand the situation, water quality parameters such as Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Evaluation Index (HEI) have also been assessed for all the sites close to industrial hub. Among all sites except at A3 and A6, WQI is found to be much greater than WHO and BIS established limit. Level of arsenic in the water at A1 location was found 73 ppb. However, lead metal in water was found to be very high at all the six studied locations, and at A1 location, it is found extremely high 2613 ppb. Therefore, water at A1 and A2 sites is found to be unfit for drinking. Practitioner Points Total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits prescribed by the WHO Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Enrichment Index (MEI) have also been assessed for all the sites Water at some sites is found unfit for drinking purpose. Based on the observations, some remedial measures are suggested to reduce the level of water contaminants up to desired levels.

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