The crude methanolic extract of Datura stramonium, Azadirachta indica, and Calotropis procera leaves, Allium sativum (AS) cloves, and Carica papaya (CP) seeds collected from Banaskanta, Gujarat (India) was tested for its acaricidal properties against Rhipicephalus (Boophilus) microplus. The percent adult mortality within 15 days, reproductive index, percentage inhibition of oviposition, hatching of laid ova, and percentage larval mortality were studied at concentrations of 12.5, 25, 50, and 100 mg/ml. At the highest concentration (100 mg/ml), the adult tick mortality was 66.67, 73.33, 80.00, and 93.33% for C. procera, D. stramonium, A. sativum, and C. papaya extracts, respectively, and it was statistically significant (P < 0.001). However, for A. indica, mortality was low and estimated to be 33.33%. Inhibition of oviposition at the highest concentration of A. indica, C. procera, D. stramonium, A. sativum, and C. papaya extract-treated ticks was 20.73, 71.34, 77.17, 85.83, and 100.00%, respectively. Inhibition of fecundity of treated groups differed significantly from the control and was concentration dependent. Larvae treated with all the tested concentrations of A. indica, C. procera, D. stramonium, A. sativum, and C. papaya extracts by larval packet test showed significant mortality (P < 0.001) than that of control tick larvae, and at the highest concentration, it was 55.2, 63.2, 71.8, 69.0, and 82.2%, respectively. Garlic cloves and papaya seed extract produced complete failure of eclosion of eggs from the treated ticks even at lower concentrations; however, neem, calotropis, and datura was capable of reducing hatchability by 20, 50, and 70%, respectively. The results pointed that the crude extracts of A. sativum cloves and C. papaya seeds have very good acaricidal properties and could be a potential component of alternative R. (B.) microplus tick control strategy.

About 80 % of world cattle population is under the risk of ticks and tick borne diseases (TTBDs). Losses caused by bovine tick burdens in tropical countries have a tremendous economic impact on production systems. Chemical control of disease has been found to be ineffective and also involving large cost. To reduce our reliance on these chemical products, it is necessary to embark on programs that include habitat management, genetic selection of hosts, and development of a strain capable of inducing host resistance to ticks. Selection for disease resistance provide alternate method for sustainable control of TTBDs. Domestic livestock manifests tick-resistance by skin thickness, coat type, coat color, hair density and skin secretions etc. Zebu cattle have, on average, greater tick resistance than either European cattle or African cattle. Heritability for tick burden in cattle has been shown to range about 0.30, which is sufficient to result in the success of some programs of selection for tick resistance in cattle. To select animals at younger age, to reduce generation interval and to increase genetic gain, marker assisted selection is an important tool. There are also various MHC molecules which are associated with resistance to TTBDs.

Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising

Rhipicephalus (Boophilus) microplus is the most common tick species in India infesting cattle and buffaloes and causing significant economic losses to dairy and leather industries by adversely affecting the milk production and quality of hides. A study to evaluate the acaricide resistance status of Rhipicephalus (Boophilus) microplus to deltamethrin, flumethrin, and fipronil was conducted on the samples collected from organized and unorganized farms of North Gujarat state, where treatment failures were reported frequently. Adult Immersion Test (AIT) and Larval Packet Test (LPT) were conducted using field strain for determination of 50 and 95% lethal concentration of deltamethrin, flumethrin, and fipronil. Results obtained by the Adult Immersion Test showed low grade resistance (level I, RF > 5) has been developed against both deltamethrin and fipronil. However, deltamethrin by performing Larval Packet Test showed moderate grade resistance (level II, RF > 25). Larval packet performed by flumethrin also revealed low grade resistance, level I. The data on field status of acaricide resistance from the area with diversified animal genetic resources will be helpful to adopt suitable strategy to overcome the process of development of resistance in ticks.

Vision-based human activity recognition is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here the authors propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. The authors contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that their method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.