Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.

The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.

SUMMARY: Genetic variation in feed efficiency may have a significant impact on sheep production in integrated crop livestock farming systems in dry areas, where the shortage and poor quality of feed is widespread. Thus, the present study was carried out to investigate the effects of sheep genotype and feed source on liveweight gain, feed conversion efficiency and dry matter (DM) intake in feedlot lambs finished on diets based on low-cost forages or a high-cost concentrated feed as a means of assessing the efficiency of this feeding system. Early weaned lambs of the purebred fat-tailed Akkaraman breed were compared with synthetic Anatolian Merino (0·80 German Mutton Merino×0·20 Native Akkaraman) breed. The lambs were kept in individual pens for 8 weeks and fed four diets: daily harvested forages of triticale (T), Hungarian vetch (HV), a triticale-Hungarian vetch mixture (T+HV), and a concentrate-based feed (CF). Lamb liveweight gain (LWG) was monitored during the early (18 April–16 May) and late (17 May–13 June) spring periods. Diet×period and diet×breed interactions were detected in LWG of the lambs. Lambs from both genotypes on the concentrate-based diet had higher liveweight gains, DM intakes and better feed conversion ratios compared with lambs finished on the forage-based diets. The LWG of lambs offered triticale forage decreased from 177 g/head/day in the early spring to 95 g/head/day in the late spring period, as plant maturity increased. Liveweight gains did not change for the other forage rations during the same period. The LWG of Akkaraman lambs were similar for both the early (189 g/head/day) and the late (183 g/head/day) spring periods, whereas Anatolian Merino lambs gained 41 g/head/day less LW and had 3·8 higher feed conversion rate for the late spring period compared with the early spring period. The present study showed that fat-tailed Akkaraman lambs were better able to utilize forages with low nutritive value compared to Anatolian Merino lambs, and may be better suited to semi-arid areas, where crop and livestock are highly integrated in the farming system.

Underwater localization and tracking is a challenging problem and Time-of-Arrival and Time-Difference-of-Arrival approaches are commonly used. However, the performance difference between these approaches is not well understood or analysed adequately. There are some analytical studies for terrestrial applications with the assumption that the signal arrival times are not correlated. However, this assumption is not valid for underwater propagation. To present the distinct nature of the problem under the water, a high-fidelity simulation is required. In this study, Time-of-Arrival and Time-Difference-of-Arrival approaches are compared using a ray tracing based propagation model. Moreover, basic methods to mitigate the multipath propagation problem are also implemented for Bernoulli filters. Since the Bernoulli filter is a joint detection and tracking filter, the detection performance is also analysed. Comparisons are done for all combinations of filter and measurement approaches. The results can help to design underwater localization systems better suited to the needs.