Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.

Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study’s objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.

Australia, a country which suffers from recurrent droughts, is currently experiencing a shift in climate. It is often classified as the driest inhabited continent due to the extremely low annual average rainfall (465 mm) and associated low annual average runoff (57 mm). This has required a regular revision of Australia’s water policy to align with the needs of its society. Several changes in water policy have been formulated in recent times with the objective of striking a balance between the consumptive and environmental components of flows in Australian catchments. Some of the developments that affect irrigated agriculture include: (i) the Council of Australian Government’s water reforms; (ii) the Murray–Darling Basin Commission cap (the volume of water that could be diverted under 1993–94 levels of development); (iii) environmental flow rules; and (iv) the National Water Initiative. At a strategic level global climate change threatens the viability of irrigated agriculture and other industries. Under the present water reforms, longer-term water security is not guaranteed because these reforms do not explicitly take into account threats to water quantity and quality due to enhanced climate variability and change. At an operational level, current water allocation systems do not take into account state-of-the-art climate forecasting methods. Therefore, it is often not until after the irrigation season is well underway that irrigators have a reasonable knowledge of how much water will be available. Thus, there is considerable risk associated with planting and crop establishment decisions, resulting in a need for climate forecasting tools aimed at risk management. There is also a need for Australian water legislation and policy to be revisited to incorporate climate change and adaptive management options.