Poultry litter, a mix of excreta, bedding material, and discarded feed, is extracted from poultry houses, and used as fertiliser. The litter is often stored in stockpiles outside before field application thereby posing a risk for negative environmental and climatic impact from emissions of ammonia (NH3) and greenhouse gases (GHG). This study investigated the emissions of methane (CH4), NH3, and nitrous oxide (N2O) from a 22 tonnes broiler litter stockpile over 44 days. The emissions were measured on a farm-scale stockpile with and without coverage using the backward Lagrangian Stochastic method. The results showed distinct emission patterns for each gas during the measurement periods. For all compounds, the emissions during the covered period were significantly lower than during the two uncovered periods. The reduction due to coverage was 92–95% for NH3, 25–40% for CH4, and 82–89% for N2O. NH3 emissions were highest immediately after coverage removal and during stockpile removal. CH4 emissions were highest during stockpile removal and lowest during coverage. N2O emissions were lowest during coverage but a notable increase after coverage removal was observed. The temperature within the stockpile showed variations at different heights, with the highest temperatures recorded in the middle of the stockpile. GHG emissions, based on global warming potential, indicate substantial contributions from N2O, accounting for 55–72% of emissions in CO2-equivalents during uncovered periods and 27% during coverage. Furthermore, GHG emissions were reduced 63–72% during coverage compared to the uncovered periods highlighting the importance for immediate coverage of stockpiles to minimise NH3 and GHG emissions. Science4Impact statementThe findings of this study offer clear, actionable insights for poultry farm operators and environmental regulators seeking to reduce harmful emissions from broiler litter stockpiles. This reduction not only limits the environmental impact but also minimizes nutrient losses.For farm operators, implementing immediate and continuous coverage of stockpiles can be an efficient, cost-effective strategy to comply with environmental regulations and reduce greenhouse gas emissions. For policymakers, these results provide a basis to recommend or enforce stockpile covering as a best practice for mitigating agricultural emissions, contributing to climate action and improved air quality.

Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.

Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and de-leafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents an alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, the role of attention in improving targeted perception using an attention-driven NBV planning strategy was investigated. Through simulation experiments using plants with high levels of occlusion and structural complexity, it was shown that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, it was shown that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production.