Michael G. K. Jones

Gene editing (GEd) technologies are rapidly transforming agricultural biotechnology; however, their regulatory treatment remains ambiguous under international instruments such as the Cartagena Protocol on Biosafety (CPB), which was originally developed for genetically modified organisms (GMOs). This regulatory uncertainty creates challenges for product developers and regulators. This study critically examines the role of the Precautionary Principle (PP) in governing emerging genetic technologies. While the PP underpins the CPB, its interpretation, particularly in the European Union, has been criticized for creating legal barriers that have delayed the adoption of beneficial technologies. In contrast, a Principle-Based Approach (PBA) provides a more adaptive governance framework, grounded in high-level principles that enable flexibility with evolving scientific evidence. Through a review of global regulatory trends, this study identifies jurisdictional challenges and contrasts the theoretical and practical implications of the PP and PBA. It concludes with policy recommendations advocating a hybrid model integrating precaution and principle-based flexibility.

Artificial intelligence (AI) and deep learning (DL) for plant disease detection are emerging research areas. DL methods generally require a large amount of annotated data for training, which is often costly, time-consuming, and infeasible. This article addresses the data scarcity problem and proposes a few-shot learning (FSL) method for barley plant disease detection. To prepare a dataset, we captured images from outdoor test-bed trials (at two different growth stages of plants across multiple paddocks) under various weather conditions, such as sunny and cloudy. The images are divided into patches and manually labelled into five classes: no-disease, net form net blotch (NFNB) (which is classified into two stages: early and severe), spot form net blotch (SFNB), and scald. We name this as the Barley dataset. We also used the publicly available cassava dataset, which has five classes. The datasets are then applied to the proposed FSL pipeline, which only uses as few as five images for each class in training. We use the Swin transformer as the network backbone. The method with the Swin-B variant as the feature extractor achieved a detection accuracy of 91.80% and 97.93% on the barley disease and cassava datasets, respectively. The result indicates that our FSL model can efficiently perform and classify barley disease with small training data.
•A few-shot learning method is developed to address data scarcity problems.•Results on the collected plant disease data warrant the model’s potential.•Cutting-edge transformers, e.g. Swin-B, perform well given only five training images•Meta-training and transfer learning significantly improve performance.•Apparent disease symptoms can be detected and used in various applications.

Automatic weed detection and classification can significantly reduce weed management costs and improve crop yields and quality. Weed detection in crops from imagery is inherently a challenging problem. Because both weeds and crops are of similar colour (green on green), their growth and texture are somewhat similar; weeds also vary based on crops, geographical locations, seasons and even weather patterns. This study proposes a novel approach utilising object detection and meta-learning techniques for generalised weed detection, transcending the limitations of varying field contexts. Instead of classifying weeds by species, this study classified them based on their morphological families aligned with farming practices. An object detector, e.g., a YOLO (You Only Look Once) model is employed for plant detection, while a Siamese network, leveraging state-of-the-art deep learning models as its backbone, is used for weed classification. This study repurposed and used three publicly available datasets, namely, Weed25, Cotton weed and Corn weed data. Each dataset contained multiple species of weeds, whereas this study grouped those into three classes based on the weed morphology. YOLOv7 achieved the best result as a plant detector, and the VGG16 model as the feature extractor for the Siamese network. Moreover, the models were trained on one dataset (Weed25) and applied to other datasets (Cotton weed and Corn weed) without further training. The study also observed that the classification accuracy of the Siamese network was improved using the cosine similarity function for calculating contrastive loss. The YOLOv7 models obtained the mAP of 91.03 % on the Weed25 dataset, which was used for training the model. The mAPs for the unseen datasets were 84.65 % and 81.16 %. As mentioned earlier, the classification accuracies with the best combination were 97.59 %, 93.67 % and 93.35 % for the Weed25, Cotton weed and Corn weed datasets, respectively. This study also compared the classification performance of our proposed technique with the state-of-the-art Convolutional Neural Network models. The proposed approach advances weed classification accuracy and presents a viable solution for dataset independent, i.e., site-independent weed detection, fostering sustainable agricultural practices.

Background
Potato is the most widely grown tuber crop worldwide and a staple food in many countries: it has become the focus of many molecular breeding studies. One topical area is breeding potato seeds, especially advancing male sterile plants, focusing on developing cytoplasmic male sterility (CMS) as a breeding tool. A major obstacle has been the identification of mitochondrial genes for CMS. Quantifying the expression of candidate CMS genes is a critical aspect needed for the validation of gene expression levels for all organisms, and quantitative real-time polymerase chain reaction (qRT-PCR) is a powerful tool for this purpose. However, selecting appropriate internal control genes for normalization of mitochondrial gene expression presents specific challenges. The aim of this study was to identify suitable reference genes best suited for analysis of mitochondrial gene expression in different tissues and developmental stages of potato, particularly in developing anthers.

Results
We assessed the expression of eighteen candidate internal control genes, including four previously studied nuclear reference genes and fourteen mitochondrial candidate reference genes. By studying gene expression in a range of tissues, the genes nad1 and nad2 were found to be the most stable reference genes, since they were expressed most consistently using four different analytical tools, GeNorm, Delta Ct, Bestkeeper and NormFinder. In contrast, expression levels of the conventional nuclear reference genes were more variable. The comprehensively ranked first candidate gene, nad2 is proposed as the preferred choice as a reference gene, especially when studying different stages of anther development. Notably, actin, the most widely used marker expression gene, worked well in some cases, but there was significant variation in its rankings, for example, using the Bestkeeper tool it was ranked sixth.

Conclusions
The results indicate that nad1 and nad2 respectively were the most stably expressed marker genes in 8 different tissues and stages of anther development. This study provides valuable support for future research on mitochondrial gene expression in potato, specifically for identifying patterns of expression of CMS genes, and can be a valuable tool to quantify gene expression for other Solanaceae species.

Root-lesion nematodes (Pratylenchus spp.) reduce the yield and quality of cereal crops in Australia. Eleven of the ~90 species characterised are present in Aus-tralia, with those determined as economic pests of broadacre agriculture costing an estimated AUD 250 million annually. Two species, P. curvicauda and P. quasitereoides, recently re-described, were isolated from fields located in the grainbelt of Western Australia, but little is known about their distribution in the region surveyed in this study. To investigate this and possible co-infestations with other Pratylenchus spp., we surveyed seven commercial wheat, barley, and oat farms near Katanning, Cancanning, Kenmare, Duranillin, Darkan, and a barley seed-bulk nursery near Manjimup, all in the southwest grainbelt of Western Australia. Morphological and molecular charac-terisation of Pratylenchus spp. extracted from soil and plant roots indicated all fields surveyed were infested. Both P. quasitereoides and P. curvicauda were present as single or mixed populations with P. penetrans and/or P. neglectus, although they were not found in the same field. Analyses of the D2–D3 sequences of the identified nematodes indicated that the species found in Australia were distinct, particularly P. quasitereoides and P. curvicauda. This work suggests P. curvicauda is likely to be present more widely in the WA grainbelt. Expanding molecular diagnostic testing for Pratylenchus species in the region to account for both nematodes is urgently needed so effective management can be implemented.

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

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.

Aphids are persistent insect pests that severely impact agricultural productivity. The detection of aphid infestations is critical for mitigating their effects. This paper presents an artificial intelligence approach to detect aphids in crop images captured by consumer-grade RGB imaging cameras. In addition to detecting the presence of aphids, the size of the aphid is an important indicator of infestation severity. To address these, we present a Bayesian multi-task learning model to detect the presence of aphids and estimate their size simultaneously.

Our model employs a joint loss function, combining a classification loss and a customised size loss. The classification component aims to identify images containing aphids, whilst the customised size loss function estimates the size of the aphids. The latter is specifically designed to account for discrepancies between the estimated and actual ground truth sizes, enhancing the accuracy of the size estimation. The model utilizes a ResNet18 backbone, ensuring robustness and adaptability across various conditions.

The proposed model was evaluated using an agricultural pest dataset consisting of images of corn, rape, rice, and wheat crops. It achieved aphid presence detection accuracies of 75.77%, 66.39%, 70.01%, and 59% for corn, rape, rice, and wheat images, respectively. An in-depth evaluation of predictive uncertainties revealed areas of high confidence and potential inaccuracies for both size and presence of aphids in images, offering insight for future model refinement. We also conducted an ablation study to thoroughly analyse the contributions of each component in proposed model.

Our model offers a valuable tool that can be used in pest management strategies for facilitating more sustainable and efficient agricultural practices.

Weeds can decrease yields and the quality of crops. Detection, localisation, and classification of weeds in crops are crucial for developing efficient weed control and management systems. Deep learning (DL) based object detection techniques have been applied in various applications. However, such techniques generally need appropriate datasets. Most available weed datasets only offer image-level annotation, i.e., each image is labelled with one weed species. However, in practice, one image can have multiple weed (and crop) species and/or multiple instances of one species. Consequently, the lack of instance-level annotations of the weed datasets puts a constraint on the applicability of powerful DL techniques. In the current research, we construct an instance-level labelled weed dataset. The images are sourced from a publicly available weed dataset, namely the Corn weed dataset. It has 5997 images of Corn plants and four types of weeds. We annotated the dataset using a bounding box around each instance and labelled them with the appropriate species of the crop or weed. Overall, the images contain about three bounding box annotations on average, while some images have over fifty bounding boxes. To establish the benchmark dataset, we evaluated the dataset using several DL models, including YOLOv7, YOLOv8 and Faster-RCNN, to locate and classify weeds in crops. The performance of the models was compared based on inference time and detection accuracy. YOLOv7 and its variant YOLOv7-tiny models both achieved the highest mean average precision (mAP) of 88.50% and 88.29% and took 2.7 and 1.43 ms, respectively, to classify crop and weed species in an image. YOLOv8m, a variant of YOLOv8, detected the plants in 2.2 ms with the mAP of 87.75%. Data augmentation to address the class imbalance in the dataset improves the mAP results to 89.93% for YOLOv7 and 89.39% for YOLOv8. The detection accuracy and inference time performed by YOLOv7 and YOLOv8 models in this research indicate that these techniques can be used to develop an automatic field-level weed detection system.

Effective use of GPS and mobile networks for localisation in rangeland areas is constrained by their high power consumption and high deployment costs. Long-range (LoRa), a low-power wide area network (LPWAN) technology, can be employed to mitigate these challenges. In contrast to prior research where the prevalent approaches entail multiple gateways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning-based pipeline is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to reduce the impact of noise on the RSSI values. Then, several machine learning techniques, such as support vector machines, random forest, and k-nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudo-trilateration method. The proposed method was tested in a real-world semi-line-of-sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two forms of experiments were performed: active searching and passive monitoring. We propose an iterative estimation process to address the dilution of precision caused by the initial positions of the gateway required for active searching applications. The results show that active searching typically requires 2 to 3 hops to reach a target node. The accuracy of passive monitoring depends on the proximity of the gateway, which varies from 20 m to 170 m. This proposed approach has the potential to open the way for localising, tracking, or monitoring target objects within sparsely populated rangeland areas, even when resources are severely constrained.