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This study aimed to determine the percentage infestation and population parameters of the green peach aphid Myzus persicae under laboratory conditions and evaluate the effect of aqueous extracts of three different plants (pot marigold: Calendula officinalis, mint: Mentha viridis and rosemary: Salvia rosmarinus) on the mortality of this aphid. Extracts of these plants were used at three concentrations (C1 = 15%, C2 = 30% and C2 = 45%). Results indicate a percentage infestation of 76.9 ± 9.4%, a mean relative growth rate of 0.062 ± 0.007 and generation time of 11.12 ± 1.42 days. All treatments reduced the numbers of aphids and statistically significantly reduced the number (α < 0.01) recorded after treatment with C1, C2 and C3 of each extract. C. officinalis extract was more effective than those of M. viridis and S. rosmarinus. The highest mortality (69.82 ± 5.23%) and efficacy (61.71 ± 4.46%) were recorded for the C3 of aqueous extract of C. officinalis, whereas the lowest mortality (38.24 ± 2.42%) and efficacy (32.41 ± 1.23%) were recorded for the C1 of extract of M. viridis. The data provided indicate that aqueous extracts of C. officinalis, M. viridis and S. rosmarinus have an insecticidal effect on M. persicae and can be integrated into a pest management strategy to reduce M. persicae abundance on pepper plants.

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Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is $O(1)$ . Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.