An effective pest management approach in potato to combat insect pests and herbicide

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

Pest management of postharvest potatoes: lethal, sublethal and transgenerational effects of the ectoparasitic mite Pyemotes zhonghuajia on the potato worm Phthorimaea operculella

BACKGROUND

Potato, Solanum tuberosum, is one of the most important food crops in the world, playing a significant role in global food security. However, many potato industries and farms may suffer losses of tuber yield and quality in storage due to lepidopteran pests. Here, we evaluated the effectiveness of an ectoparasitic idiobiont mite Pyemotes zhonghuajia in the biological control of the potato tuber moth (PTM) Phthorimaea operculella by determining the lethal, sublethal (nonconsumptive) and transgenerational effects of P. zhonghuajia of various population densities and exposure durations on PTM survival, development and reproduction.

RESULTS

Pyemotes zhonghuajia females were capable of killing all instar stages of PTM, while resistance to mite parasitism increased with the development of PTM life stage. The mortality of mature larvae (i.e., fourth instar) and pupae increased with increasing mite density and exposure duration. P. zhonghuajia imposed significant negative sublethal impacts on PTM pupation rate, female fecundity and adult longevity but not on immature development. The sublethal stress was transgenerational, resulting in lower reproduction in the offspring generation.

CONCLUSION

P. zhonghuajia induces lethal, sublethal and transgenerational effects and significantly decreases PTM survival and reproductive out, demonstrating its high efficiency in the biological control of PTM. Our study provides insight into the mechanisms underlying the nonconsumptive effects of parasitism in an ectoparasite-host system and delivers critical information for the design and implementation of augmentative releases of P. zhonghuajia in the biological control of PTM in potato storage. © 2023 Society of Chemical Industry.

An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model's effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production.

Low Mismatch Rate between Double-Stranded RNA and Target mRNA Does Not Affect RNA Interference Efficiency in Colorado Potato Beetle

RNA interference (RNAi)-based technology has been proven as a novel approach for insect pest control. However, whether insects could evolve resistance to RNAi and the underlying mechanism is largely unknown. The target gene mutations were thought to be one of the potential ways to develop the resistance. Here we predicted the effective siRNA candidates that could be derived from dsRNA against the Colorado potato beetle (CPB) β-Actin gene (dsACT). By site-directed mutagenesis, we synthesized the dsRNAs with the defect in generation of effective siRNAs (and thus were supposed to have comparable low RNAi efficacy). We showed that, with mismatches to the target gene, all the dsRNA variants caused similar levels of silencing of target gene, mortality and larval growth retardation of CPB. Our results suggest that when the mismatch rate of dsACT and target β-Actin mRNA is less than 3%, the RNAi efficiency is not impaired in CPB, which might imply the low possibility of RNAi resistance evolving through the sequence mismatches between dsRNA and the target gene.

Development of Efficient, Reproducible and Stable Agrobacterium-Mediated Genetic Transformation of Five Potato Cultivars

The developments in transformation technology have enabled the scientists to incorporate, mutate or substitute gene(s) leading to a particular trait; advancing it to a point where only few technical limitations remain. Genotype dependency and explant types are important factors affecting transformation efficiency in potato. In the present study, a rapid, reproducible and stable Agrobacterium-mediated transformation procedure in potato was developed by a combination of different plant growth regulators. Leaf discs and internodal explants of five cultivars of potato, i.e. Lady Olympia, Granola, Agria, Désirée and Innovator were infected with Agrobacterium tumefaciens strain LBA4404 containing pBIN19 expression vector with β-glucuronidase gusA gene under the control of 35S CaMV promoter. Kanamycin was used as plant selectable marker for screening of primary transformants at concentration of 100 mg/L. Both explants responded positively; internode being more suitable explant for better transformation efficiency. Based on GUS histochemical assay, the transformation efficiency was 22, 20, 18.6, 15 and 10% using the internodal explant, and 15, 12, 17, 8 and 6% using leaf discs as explant in Lady Olympia, Granola, Agria, Désirée and Innovator respectively. Furthermore, PCR assays confirmed the presence of gusA and nptII genes in regenerated plants. The molecular analysis in succeeding progeny showed proper integration and expression of both genes. The results suggest Lady Olympia as the best cultivar for future transformation procedures. Overall, the short duration, rapidity and reproducibility makes this protocol suitable for wider application of transgenic potato plants.

Downregulation of imidacloprid resistant genes alters the biological parameters in Colorado potato beetle, Leptinotarsa decemlineata Say (chrysomelidae: Coleoptera)

Colorado potato beetle, Leptinotarsa decemlineata Say (coleoptera: chrysomelidae), is the important pest of potato all over the world. This insect pest is resistant to more than 50 active compounds belonging to various chemical groups. Potential of RNA interference (RNAi) was explored to knock down transcript levels of imidacloprid resistant genes in Colorado potato beetle (CPB) under laboratory conditions. Three important genes belonging to cuticular protein (CP), cytochrome P450 monoxygenases (P450) and glutathione synthetase (GSS) families encoding imidacloprid resistance were targeted. Feeding bio-assays were conducted on various stages of imidacloprid resistant CPB lab population by applying HT115 expressing dsRNA on potato leaflets. Survival rate of insects exposed to CP-dsRNA decreased to 4.23%, 15.32% and 47.35% in 2nd, 3rd and 4th instar larvae respectively. Larval weight and pre-adult duration were also affected due to dsRNAs feeding. Synergism of RNAi with imidacloprid conducted on the 2nd instar larvae, exhibited 100% mortality of larvae when subjected to reduced doses of GSS and CP dsRNAs along with imidacloprid. Utilization of three different dsRNAs against imidacloprid resistant CPB population reveal that dsRNAs targeting CP, P450 and GSS enzymes could be useful tool in management of imidacloprid resistant CPB populations.