This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.

With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.

Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (k_c). The ET-driven irrigation decisions for grapevines rely on the sampling of several k_c values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate k_c at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model k_c using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with k_c throughout the season (p ≤ 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post véraison—the onset of ripening in grapes. Combining structural and spectral information improved the model’s performance. Among the investigated predictive models, the random forest predicted k_c with the highest accuracy (R²: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of k_c within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of k_c can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.

This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale.

A dual-field-of-view spectroradiometer system has been developed for measuring solar-induced chlorophyll fluorescence (SIF), from an unmanned aircraft system (UAS). This ``AirSIF'' system measures spectral reflectance in the visible and near-infrared wavelengths as well as SIF in far-red O₂-A and red O₂-B absorption features at high spatial resolution. It has the potential to support the interpretation and validation of canopy-emitted SIF observed by airborne, and future spaceborne sensors at coarser spatial resolutions, as well as simulated by radiative transfer models. In this contribution, we describe the AirSIF data collection and processing workflows and present a SIF map product of spatially explicit and geometrically correct spectroradiometer footprints. We analyze two possible sources of error in SIF retrieval procedure: a sensor-specific spectral artifact called etaloning and the uncertainty of incoming irradiance during UAS flight due to airframe motion (pitching and rolling). Finally, we present results from two SIF acquisition approaches: a continuous mapping flight and a stop&go flight targeting predefined areas of interest. The results are analyzed for a case study of Alfalfa and grass canopies and validated against ground measurements using the same system.