Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes.

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Root-knot nematodes ( Meloidogyne spp.; RKN) are major plant-parasitic nematodes that cause significant loss to agricultural production. An accurate assessment of the RKN population density at a field level is crucial for decisions about the application of control measures to minimise yield losses. Traditionally, RKN populations are identified and counted by nematologists using a microscope. This method is a specialised, time-consuming process and prone to errors. In our study, we investigated three semi-automated methods to detect and count RKN eggs using image analysis: contour arc (CA), skeleton structure (SS), and extreme point (EP). These methods were used to automate the length measurement of RKN eggs, and the results were compared with traditional methods of quantification. The EP method produced the highest correlation with the manual length measurement of RKN eggs. Further, these methods were used to detect and count RKN eggs to quantify low and highly cluttered images. We estimated the optimal range of the ratio of each method to detect and count RKN eggs. Overall, the EP method computed using mid-width of RKN eggs revealed better detection and counting of RKN eggs as compared to the SS and CA methods. A counting correlation up to R ² = 0.905 was obtained. This study found that the difference between mid-width and the average width of RKN eggs and soil particles could be used to discriminate 70-80 % of soil particles. Our research thus contributes a new feature that can be used to discriminate or classify objects in object detection techniques.

Tomato is the most popular vegetable globally. However, in certain conditions, the vegetable is susceptible to plant parasites such as the root-knot nematode (RKN; Meloidogyne spp.). A proper detection method is required to identify RKN and eliminate related diseases. The traditional manual quantification of RKN using a microscope is a time-consuming and laborious task. This study aims to develop a semi-automated method to discern and quantify RKN based on size using an image analysis method. The length of RKN was assessed using three novel approaches: contour arc (CA), thin structure (TS), and skeleton graph (SG) methods. These lengths were compared with the manual measurement of RKN length. The study showed that the RKN length obtained by manual measurement was highly correlated to the length based on this method, with R² of 0.898, 0.875, and 0.898 for the CA, TS, and SG methods, respectively. These approaches were further tested to detect RKN on 517 images. The manual and automated counting comparison revealed a coefficient of determination R² = 0.857, 0.835 and 0.828 for CA, TS, and SG methods, respectively. The one-way ANOVA test on counting revealed F-statistic = 4.440 and p-value = 0.004. The ratio of length to width was investigated further at different ranges. The optimal result was found to occur at ratio range between 10–35. The CA, TS, and SG methods attained the highest R² of 0.965, 0.958, and 0.973, respectively. This study found that the SG method is most suitable for detecting and counting RKN. This method can be applied to detect RKN or other nematodes on severely infected crops and root vegetables, including sweet potato and ginger. The study significantly helps in quantifying pests for rapid farm management and thus minimise crop and vegetable losses.

Sesame (Sesamum indicum L.) is increasingly recognised as a drought-tolerant oilseed crop, particularly important for water-limited agriculture. However, evidence supporting its drought resilience is scattered across various studies in physiology, agronomy, genetics, and environmental science. This review aims to synthesise current understanding of sesame’s water use, responses to drought, irrigation management, water-use efficiency, interactions with nutrients, disease dynamics, breeding strategies, and adaptation to climate change. Sesame has several mechanisms to adapt to water deficits, such as deep rooting, stomatal regulation, osmotic adjustment, efficient carbon allocation, and the ability to recover after rewatering. However, these traits offer only partial protection against drought. Research shows that flowering and early capsule development are particularly sensitive to drought, with water shortages during these stages leading to significant yield losses. Strategic application of moderate deficit or supplemental irrigation can enhance water productivity, although the effectiveness varies among different genotypes and environmental conditions. The review points out major knowledge gaps, including inconsistent metrics for water-use efficiency, a lack of studies examining genotype x irrigation interactions, limited understanding of drought–disease relationships, and an over-reliance on pot experiments and short-term studies. Evidence from field-scale research, especially in emerging production regions like Australia, is still lacking. In summary, sesame should be viewed as drought-resilient rather than drought-proof. Future advancements will rely on integrating drought-resilient breeding, precision irrigation, root phenotyping, remote sensing, crop modelling, and improved management of water and nutrients within a genotype x environment x management framework, thereby strengthening the role of sesame in climate-resilient and sustainable farming systems under increasing water scarcity.