Sayma Shammi

Assessing spatial and temporal variations in crop water stress is vital for precision irrigation. This study utilized Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MSS) and thermal band (TB) sensors to map the crop water stress index (CWSI) in wheat. A water deficit experiment was conducted on winter wheat under varying irrigation levels during late vegetative, reproductive, and maturation stages. CWSI was calculated using canopy temperature, ambient air temperature, and vapor pressure deficit (VPD). Six machine learning (ML) models-linear model (LM), random forest (RF), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGB), and artificial neural network (ANN)-were developed for pre-heading, post-heading, and seasonal datasets. The top five vegetation indices (VIs), selected using Recursive Feature Elimination (RFE), along with thermal data, were used as inputs to the ML models. Results showed that seasonal ML models outperformed those based only on pre-heading or post-heading data. Particularly, the RF model performed well, with respective R-2 and RMSE values of 0.87 and 0.09 for seasonal, 0.82 and 0.05 for pre-heading, and 0.93 and 0.06 for post-heading datasets. SHapley Additive exPlanations (SHAP) analysis identified Red Normalized Value (RNV), TB, and Green Red Vegetation Index (GRVI) as key predictors of CWSI in the RF model. CWSI maps effectively captured spatial variations in water stress, aligning with irrigation management practices. This study demonstrates the effectiveness of combining UAV remote sensing and ML for precision irrigation management.

Australia is emerging as a hub for Agriculture 4.0; however, adoption of agricultural technologies (AgTech) remains limited due to economic constraints, infrastructure gaps, fragmentation, and interoperability challenges. This study aims to categorise the diverse range of commercially available AgTech products and map their distribution across data acquisition platforms, crop management practices, and the precision agriculture cycle, while also exploring potential adoption barriers based on their market availability. We classified data acquisition platforms utilised by the current commercial products. Then, we categorised the major crop management practices in grain farming. We also proposed a framework of the stages of precision agriculture that align with existing market products. We developed a novel taxonomy to categorise the technologies utilised by the AgTech industry for grain farming. We searched four Australian AgTech databases using a search strategy to capture all relevant commercial products related to the proposed crop management categories. In this survey, we categorised 80 AgTech products that provide tangible solutions to current farming practices based on the proposed taxonomy. We also analysed the current trends by mapping products across data acquisition platforms, crop management practices, and the precision agriculture cycle, as well as the types of technologies. Results show that commercial AgTech products mostly rely on ground-based stationary platforms for data acquisition, focus on crop protection, integrate proprietary hardware, and emphasise data visualisation rather than generating actionable insights. This finding also suggests that the high prevalence of proprietary hardware integration may indicate interoperability challenges, a significant barrier to AgTech adoption. Overall, this survey maps the breadth of commercially available AgTech in the grains industry and also discusses the opportunities for future research and innovation.

Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic process. During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding these temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected infrared thermal (IRT) images from field-grown wheat crops and extracted the temperatures from the canopy, plant and ground layers. We analysed these temperatures and applied four machine learning (ML) models to detect coldness scales leading to frost nights with different degrees of severity. We implemented a gated recurrent unit, convolutional neural network, random forest and support vector machines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-frost winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not converge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures conducive to frost formation of a certain severity degree. Our results show that the ML models can determine the coldness scales automatically with 93%–98% accuracy across the four models. The study presents a strong foundation for the development of early frost detection systems.

Frost is an extreme temperature event that significantly impacts crops, particularly in Mediterranean-type climates. Current frost damage assessment techniques are heavily dependent on traditional temperature logger data and manual inspection of the crops after a suspected frost event, an approach that can be erroneous, labour-intensive and can lead to delayed management decisions. This study investigates a new technique to automatically detect two crucial stages of frost in on-field plants, i.e., exposure to freezing temperatures with and without ice formation (crystallisation and supercooling), using machine learning (ML) models trained on infrared thermal (IRT) images. Our dataset consists of IRT images of on-field wheat plants collected during the winter growing season. We demonstrate that our approach based on classification accuracy curves, can detect ice nucleation and freezing point temperatures with four ML models, extreme gradient boosting (XGBoost), random forest (RF), convolutional neural networks (CNN) and ResNet-50. We find that RF detects frost events, i.e., crystallisation for frost and supercooling for non-frost night from the accuracy curves with fastest classification time (approx. 17 ms per image). Our study provides important insights into a primary building block for the future development of automatic and real-time on-field plant frost monitoring systems.
•Machine learning models are used to detect frost in plants from thermal images.•The models are tested on field-collected infrared thermal images wheat plants.•Results show the models can detect freezing i.e., crystallisation & supercooling.•High performance indicate suitability for automatic and real-time frost detection.

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring.

Frost can cause irreversible damage to plant tissue and can significantly reduce yields and quality. A thorough understanding of the freezing dynamics is crucial to developing strategies for frost protection and the prevention of freezing damage. This study investigated artificial intelligence machine learning (ML) models to capture the thermodynamic patterns of freezing based on infrared thermography (IRT) imagery, which would help to automate the image analysis process in real-time or post frost events. A small-scale dataset of IRT images was collected to capture the freezing process from sample droplets containing ice-nucleating bacterium. We performed several ML experiments on the data to detect the transitions in temperatures from the images. We evaluated five popular ML models, namely support vector machines, random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron, and convolutional neural networks. We analysed the dataset to classify adjacent temperature transitions. The results show that ML models can consistently capture the thermodynamics of frost events, i.e., ice-nucleation and freezing points on typical freezing curves. Amongst the ML models, RF and XGBoost achieved the best results, both with an average accuracy of 87–88% in classifying the temperatures. With 0.25 °C temperature transitions, RF model identified the ice nucleation and freezing points at around −2.25 °C to −2.75 °C and −4.25 °C to −4.75 °C, respectively. RF and XGBoost took about 7.3 ms and 5.5 ms time per image respectively, which indicates that these models can be used in real-time applications. Our study shows that ML models using IRT imagery can be used as an automatic real-time tool to accurately detect the critical temperatures for frost formation.