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Control interventions in sustainable pest management schemes are set according to the phenology and the population abundance of the pests. This information can be obtained using suitable mathematical models that describe the population dynamics based on individual life history responses to environmental conditions and resource availability. These responses are described by development, fecundity and survival rate functions, which can be estimated from laboratory experiments. If experimental data are not available, data on field population dynamics can be used for their estimation. This is the case of the extrinsic mortality term that appears in the mortality rate function due to biotic factors. We propose a Bayesian approach to estimate the probability density functions of the parameters in the extrinsic mortality rate function, starting from data on population abundance. The method investigates the time variability in the mortality parameters by comparing simulated and observed trajectories. The grape berry moth, a pest of great importance in European vineyards, has been considered as a case study. Simulated data have been considered to evaluate the convergence of the algorithm, while field data have been used to obtain estimates of the mortality for the grape berry moth.

The effect of cover plants on arthropod functional biodiversity was investigated in a vineyard in Northern Italy, through a 3-year field experiment. The following six ground cover plants were tested: Sweet Alyssum; Phacelia; Buckwheat; Faba Bean; Vetch and Oat; control. Arthropods were sampled using different techniques, including collection of leaves, vacuum sampling and sweeping net. Ground cover plant management significantly affected arthropod fauna, including beneficial groups providing ecosystem services like biological control against pests. Many beneficial groups were attracted by ground cover treatments in comparison with control, showing an aggregative numerical response in the plots managed with some of the selected plant species. Alyssum, Buckwheat and 'Vetch and Oat' mixture showed attractiveness on some Hymenoptera parasitoid families, which represented 72.3% of the insects collected by sweeping net and 45.7 by vacuum sampling. Phytoseiidae mites showed a significant increase on leaves of the vineyard plots managed with ground covers, in comparison with control, although they did not show any difference among the treatments. In general, the tested ground cover treatments did not increase dangerous Homoptera populations in comparison with control, with the exception of Alyssum. The potential of ground cover plant management in Italian vineyards is discussed: the overall lack of potential negative effects of the plants tested, combined with an aggregative numerical response for many beneficials, seems to show a potential for their use in Northern Italy vineyards.

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Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This task automation could lead to reduced surgeon's cognitive workload, increased precision in critical aspects of the surgery, and fewer patient-related complications. However, current DRL methods do not guarantee any safety criteria as they maximise cumulative rewards without considering the risks associated with the actions performed. Due to this limitation, the application of DRL in the safety-critical paradigm of robot-assisted Minimally Invasive Surgery (MIS) has been constrained. In this work, we introduce a Safe-DRL framework that incorporates safety constraints for the automation of surgical subtasks via DRL training. We validate our approach in a virtual scene that replicates a tissue retraction task commonly occurring in multiple phases of an MIS. Furthermore, to evaluate the safe behaviour of the robotic arms, we formulate a formal verification tool for DRL methods that provides the probability of unsafe configurations. Our results indicate that a formal analysis guarantees safety with high confidence such that the robotic instruments operate within the safe workspace and avoid hazardous interaction with other anatomical structures.

The American leafhopper Erasmoneura vulnerata, detected in Europe in the early 2000s, has recently become a pest in North-Italian vineyards. Infestations were recorded in organic and conventional vineyards despite the application of insecticides targeting other pests. Erasmoneura vulnerata completes three generations per year, and the second generation is frequently associated with large populations. The selection of appropriate active ingredients and the timing of their application is crucial for effective pest control. Field trials were carried out in Northeastern Italy, using a randomized design, to evaluate the impact of insecticides applied against other grapevine leafhoppers on E. vulnerata populations. The beginning of the second generation was selected as the best time for insecticide application. For natural products, two applications were planned. Among the selected insecticides, the most effective were acetamiprid, flupyradifurone and lambda-cyhalothrin. Regarding natural products, the most effective was kaolin which could represent an alternative to pyrethrins in organic vineyards. The identification of pest threshold levels and the evaluation of side effects of the most effective insecticides on key natural enemies occurring in vineyards are required.