Trachelospermum jasminoides (Lindl.) Lem. Is widely used in landscaping. In August 2023, leaf spot disease in T. jasminoides was observed in the city parks and foresty districts of Nanjing, Jiangsu Province, China. Symptoms of the disease include gray‒white spots of different sizes and round or irregular shapes, and late leaf spots are very prone to crumbling, the formation of perforation symptoms, and leaf scorching and shedding. Pathogenic fungi were isolated from diseased leaves and identified as Fusarium concentricum and F. verticillioides on the basis of morphological features and multilocus phylogenetic analyses of the internal transcribed spacer (ITS) region, RNA polymerase II subunit B (RPB2), calmodulin (CaM), β-tubulin (TUB2) and translation elongation factor 1-alpha (TEF1-α) gene sequences. Pathogenicity tests revealed that F. concentricum and F. verticillioides can cause leaf spot on T. jasminoides. This study is the first report of F. concentricum and F. verticillioides causing leaf spot on T. jasminoides in China and provides a solid theoretical foundation for the scientific prevention and control of this disease.
{"title":"First report of Fusarium concentricum and F. verticillioides causing leaf spot on Trachelospermum jasminoides in China","authors":"Mengting Jiang, Yifei Wang, Qiuqin Wang, Xiuyu Zhang, Yu Wan, Yinjuan Zhao","doi":"10.1016/j.cropro.2024.107071","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107071","url":null,"abstract":"<ce:italic>Trachelospermum jasminoides</ce:italic> (Lindl.) Lem. Is widely used in landscaping. In August 2023, leaf spot disease in <ce:italic>T. jasminoides</ce:italic> was observed in the city parks and foresty districts of Nanjing, Jiangsu Province, China. Symptoms of the disease include gray‒white spots of different sizes and round or irregular shapes, and late leaf spots are very prone to crumbling, the formation of perforation symptoms, and leaf scorching and shedding. Pathogenic fungi were isolated from diseased leaves and identified as <ce:italic>Fusarium concentricum</ce:italic> and <ce:italic>F. verticillioides</ce:italic> on the basis of morphological features and multilocus phylogenetic analyses of the internal transcribed spacer (ITS) region, RNA polymerase II subunit B (RPB2), calmodulin (CaM), β-tubulin (TUB2) and translation elongation factor 1-alpha (TEF1-α) gene sequences. Pathogenicity tests revealed that <ce:italic>F. concentricum</ce:italic> and <ce:italic>F. verticillioides</ce:italic> can cause leaf spot on <ce:italic>T. jasminoides</ce:italic>. This study is the first report of <ce:italic>F. concentricum</ce:italic> and <ce:italic>F. verticillioides</ce:italic> causing leaf spot on <ce:italic>T. jasminoides</ce:italic> in China and provides a solid theoretical foundation for the scientific prevention and control of this disease.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"87 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1016/j.cropro.2024.107080
Jorge G. Hill, Paula G. Páez Jerez, Patricia C. Fernández, Facundo Herrera Linares, M Florencia Rocha, M Teresa Vera
The introduction of herbicide-resistant and Bt crops has led to significant disruption in weed and insect pest communities. Using pheromone-baited traps, we assessed the impact of varying levels of Amaranthus infestation on the population abundance of S. cosmioides in both Bt and non-Bt soybeans. Additionally, we monitored the abundance of S. cosmioides and other leaf-chewing larvae in these soybeans employing the vertical beat sheet method. There was no difference in the number of S. cosmioides adults captured by pheromone-baited traps in Bt and non-Bt soybeans. Traps placed in plots with high Amaranthus levels captured 68.3% more S. cosmioides males than those with low Amaranthus presence. Similarly, a higher number of S. cosmioides larvae were recorded in soybean plots with high Amaranthus levels. In Bt soybean, the larvae of S. cosmioides were the most abundant Lepidoptera, although its populations were surpassed in the second crop season by R. nu. In non-Bt soybean, C. includens was the dominant species throughout both crop seasons, and its larval abundance was unaffected by Amaranthus infestation levels. The presence of green bridges may facilitate the colonization of S. cosmioides in soybean crops, as adults and larvae were more abundant in soybeans with high levels of Amaranthus infestation. Moreover, the influence of other key species such as C. includens negatively affected the abundance of S. cosmioides in non-Bt soybean. A combined approach of constant monitoring and management of difficult-to-control weeds is crucial for managing polyphagous pests.
抗除草剂作物和 Bt 作物的引入极大地破坏了杂草和害虫群落。利用信息素诱捕器，我们评估了不同程度的苋属植物侵扰对 Bt 和非 Bt 大豆中宇宙大蝼蛄种群数量的影响。此外，我们还采用垂直拍片法监测了这些大豆中 S. cosmioides 和其他啃叶幼虫的数量。在 Bt 和非 Bt 大豆中，信息素诱饵诱捕器捕获的 S. cosmioides 成虫数量没有差异。在苋菜含量高的地块放置的诱捕器捕获的 S. cosmioides 雄性数量比苋菜含量低的地块多 68.3%。同样，在苋菜含量高的大豆地块中，记录到的 S. cosmioides 幼虫数量也更多。在 Bt 大豆中，S. cosmioides 幼虫是数量最多的鳞翅目昆虫，但其数量在第二季被 R. nu 超过。在非 Bt 大豆中，C. includens 在两个作物季节都是主要物种，其幼虫数量不受苋菜虫害水平的影响。由于成虫和幼虫在苋菜虫害程度较高的大豆中数量较多，绿桥的存在可能会促进 S. cosmioides 在大豆作物中的定殖。此外，其他关键物种（如 C. includens）的影响也对 S. cosmioides 在非 Bt 大豆中的数量产生了负面影响。持续监测和管理难以控制的杂草相结合的方法对于管理多食性害虫至关重要。
{"title":"Impact of Amaranthus spp. weed presence on the population abundance of Spodoptera cosmioides (Lepidoptera: Noctuidae) in genetically engineered soybean crops","authors":"Jorge G. Hill, Paula G. Páez Jerez, Patricia C. Fernández, Facundo Herrera Linares, M Florencia Rocha, M Teresa Vera","doi":"10.1016/j.cropro.2024.107080","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107080","url":null,"abstract":"The introduction of herbicide-resistant and Bt crops has led to significant disruption in weed and insect pest communities. Using pheromone-baited traps, we assessed the impact of varying levels of <ce:italic>Amaranthus</ce:italic> infestation on the population abundance of <ce:italic>S. cosmioides</ce:italic> in both Bt and non-Bt soybeans. Additionally, we monitored the abundance of <ce:italic>S. cosmioides</ce:italic> and other leaf-chewing larvae in these soybeans employing the vertical beat sheet method. There was no difference in the number of <ce:italic>S. cosmioides</ce:italic> adults captured by pheromone-baited traps in Bt and non-Bt soybeans. Traps placed in plots with high <ce:italic>Amaranthus</ce:italic> levels captured 68.3% more <ce:italic>S. cosmioides</ce:italic> males than those with low <ce:italic>Amaranthus</ce:italic> presence. Similarly, a higher number of <ce:italic>S. cosmioides</ce:italic> larvae were recorded in soybean plots with high <ce:italic>Amaranthus</ce:italic> levels. In Bt soybean, the larvae of <ce:italic>S. cosmioides</ce:italic> were the most abundant Lepidoptera, although its populations were surpassed in the second crop season by <ce:italic>R. nu</ce:italic>. In non-Bt soybean, <ce:italic>C. includens</ce:italic> was the dominant species throughout both crop seasons, and its larval abundance was unaffected by <ce:italic>Amaranthus</ce:italic> infestation levels. The presence of green bridges may facilitate the colonization of <ce:italic>S. cosmioides</ce:italic> in soybean crops, as adults and larvae were more abundant in soybeans with high levels of <ce:italic>Amaranthus</ce:italic> infestation. Moreover, the influence of other key species such as <ce:italic>C. includens</ce:italic> negatively affected the abundance of <ce:italic>S. cosmioides</ce:italic> in non-Bt soybean. A combined approach of constant monitoring and management of difficult-to-control weeds is crucial for managing polyphagous pests.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"77 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1016/j.cropro.2024.107078
Nathan H. Mercer, David R. Haviland, Kent M. Daane
The mealybug Planococcus ficus Signoret is an important vineyard pest, causing direct damage from feeding as well as vectoring important viral pathogens. Several species of ants tend P. ficus in California, including the pavement ant, Tetramorium immigrans Santschi. We tested the impact of polyacrylamide hydrogels loaded with a 25% sucrose solution and a variety of individual insecticide active ingredients (abamectin, boric acid, pyriproxyfen, spinosad, and thiamethoxam) on T. immigrans populations in vineyards. Additionally, we measured how a reduction of T. immigrans impacts densities of both P. ficus and its natural enemies. All insecticides tested reduced ant counts to some extent. Thiamethoxam was the most effective insecticide tested in both years. Boric acid was the least effective overall with pyriproxyfen and spinosad causing intermediate reductions in ant counts. In a separate trial, thiamethoxam loaded hydrogels reduced ant counts and significantly reduced P. ficus infested grape clusters, but did not reduce P. ficus vine trunk populations, nor increase parasitism rates. This is the first trial of hydrogel bait used for T. immigrans management and demonstrates the effectiveness of polyacrylamide hydrogels as ant baits and that T. immigrans reduction also lowers P. ficus abundance.
蚧壳虫 Planococcus ficus Signoret 是一种重要的葡萄园害虫，不仅会直接造成食害，还会传播重要的病毒病原体。在加利福尼亚州，有几种蚂蚁趋向于 P. ficus，包括人行道蚂蚁 Tetramorium immigrans Santschi。我们测试了含有 25% 蔗糖溶液和多种杀虫剂活性成分（阿维菌素、硼酸、吡虫啉、久效磷和噻虫嗪）的聚丙烯酰胺水凝胶对葡萄园中 T. immigrans 数量的影响。此外，我们还测量了 T. immigrans 的减少对榕树蚂蚁及其天敌密度的影响。所有测试的杀虫剂都在一定程度上减少了蚂蚁数量。噻虫嗪是这两年测试的最有效杀虫剂。硼酸的总体效果最差，吡虫啉和久效磷对蚂蚁数量的减少效果居中。在另一项试验中，含噻虫嗪的水凝胶减少了蚂蚁数量，并显著减少了受薜荔虫侵扰的葡萄簇，但没有减少薜荔虫的藤蔓数量，也没有提高寄生率。这是首次将水凝胶诱饵用于蚂蚁管理的试验，证明了聚丙烯酰胺水凝胶作为蚂蚁诱饵的有效性，而且减少蚂蚁也能降低榕树的数量。
{"title":"Mealybug, Planococcus ficus, reduction through pavement ant, Tetramorium immigrans, management using polyacrylamide hydrogel baits in vineyards","authors":"Nathan H. Mercer, David R. Haviland, Kent M. Daane","doi":"10.1016/j.cropro.2024.107078","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107078","url":null,"abstract":"The mealybug <ce:italic>Planococcus ficus</ce:italic> Signoret is an important vineyard pest, causing direct damage from feeding as well as vectoring important viral pathogens. Several species of ants tend <ce:italic>P. ficus</ce:italic> in California, including the pavement ant, <ce:italic>Tetramorium immigrans</ce:italic> Santschi. We tested the impact of polyacrylamide hydrogels loaded with a 25% sucrose solution and a variety of individual insecticide active ingredients (abamectin, boric acid, pyriproxyfen, spinosad, and thiamethoxam) on <ce:italic>T. immigrans</ce:italic> populations in vineyards. Additionally, we measured how a reduction of <ce:italic>T. immigrans</ce:italic> impacts densities of both <ce:italic>P. ficus</ce:italic> and its natural enemies. All insecticides tested reduced ant counts to some extent. Thiamethoxam was the most effective insecticide tested in both years. Boric acid was the least effective overall with pyriproxyfen and spinosad causing intermediate reductions in ant counts. In a separate trial, thiamethoxam loaded hydrogels reduced ant counts and significantly reduced <ce:italic>P. ficus</ce:italic> infested grape clusters, but did not reduce <ce:italic>P. ficus</ce:italic> vine trunk populations, nor increase parasitism rates. This is the first trial of hydrogel bait used for <ce:italic>T. immigrans</ce:italic> management and demonstrates the effectiveness of polyacrylamide hydrogels as ant baits and that <ce:italic>T. immigrans</ce:italic> reduction also lowers <ce:italic>P. ficus</ce:italic> abundance.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"48 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1016/j.cropro.2024.107077
Antonia Ângela Bezerra, Patryck Érmerson Monteiro dos Santos, Quéren Hapuque Silva Pereira de Alcantara Vilarim, Fábio Henrique Galdino dos Santos, Daniela Maria do Amaral Ferraz Navarro, Patrícia Maria Guedes Paiva, Márcia Vanusa da Silva, Thiago Henrique Napoleão, Maria Tereza dos Santos Correia
The maize weevil, Sitophilus zeamais, is one of the main primary pests of stored grains in the world. Synthetic insecticides are the main means of controlling this pest, but environmental and health issues have been associated with their use. Therefore, natural insecticides have been sought to control pests like this. The objective of this work was to evaluate the insecticidal activity of fixed oil from S. coronata seeds (FOSc) against S. zeamais through ingestion and contact toxicity tests, influence on population growth rate and assessment of residual effect. The main compounds of FOSc were lauric acid (59.88%), myristic acid (13.13%) and capric acid (9.61%). FOSc presented ingestion toxicity with lethal concentrations for 50% (LC50) and 90% (LC90) of insects of 2.58 μL/g and 8.15 μL/g, respectively. The oil was able to inhibit in vitro α-amylase activity from gut extract. In the contact toxicity, LC50 and LC90 were 2.99 μL/g and 6.21 μL/g, respectively. Treating the grains with oil reduced the emergence of insects, reducing the population growth rate. However, FOSc showed low residual effect against the insect under study, being active only for 24 h after its application. In conclusion, S. coronata fixed oil can be used as an alternative to synthetic insecticides to control S. zeamais through ingestion and contact pathways.
{"title":"Seed oil from Syagrus coronata has contact toxicity and reduces population growth of Sitophilus zeamais (Coleoptera: Curculionidae)","authors":"Antonia Ângela Bezerra, Patryck Érmerson Monteiro dos Santos, Quéren Hapuque Silva Pereira de Alcantara Vilarim, Fábio Henrique Galdino dos Santos, Daniela Maria do Amaral Ferraz Navarro, Patrícia Maria Guedes Paiva, Márcia Vanusa da Silva, Thiago Henrique Napoleão, Maria Tereza dos Santos Correia","doi":"10.1016/j.cropro.2024.107077","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107077","url":null,"abstract":"The maize weevil, <ce:italic>Sitophilus zeamais</ce:italic>, is one of the main primary pests of stored grains in the world. Synthetic insecticides are the main means of controlling this pest, but environmental and health issues have been associated with their use. Therefore, natural insecticides have been sought to control pests like this. The objective of this work was to evaluate the insecticidal activity of fixed oil from <ce:italic>S. coronata</ce:italic> seeds (FOSc) against <ce:italic>S. zeamais</ce:italic> through ingestion and contact toxicity tests, influence on population growth rate and assessment of residual effect. The main compounds of FOSc were lauric acid (59.88%), myristic acid (13.13%) and capric acid (9.61%). FOSc presented ingestion toxicity with lethal concentrations for 50% (LC<ce:inf loc=\"post\">50</ce:inf>) and 90% (LC<ce:inf loc=\"post\">90</ce:inf>) of insects of 2.58 μL/g and 8.15 μL/g, respectively. The oil was able to inhibit <ce:italic>in vitro</ce:italic> α-amylase activity from gut extract. In the contact toxicity, LC<ce:inf loc=\"post\">50</ce:inf> and LC<ce:inf loc=\"post\">90</ce:inf> were 2.99 μL/g and 6.21 μL/g, respectively. Treating the grains with oil reduced the emergence of insects, reducing the population growth rate. However, FOSc showed low residual effect against the insect under study, being active only for 24 h after its application. In conclusion, <ce:italic>S. coronata</ce:italic> fixed oil can be used as an alternative to synthetic insecticides to control <ce:italic>S. zeamais</ce:italic> through ingestion and contact pathways.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"20 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.cropro.2024.107072
Sunil Kumar Sunani, Rubin Debbarma, Bishnu Maya Bashyal, S. Raghu, B. Jeevan
{"title":"First report of Fusarium proliferatum associated with pod rot of winged bean (Psophocarpus tetragonolobus (L.) DC.) in India","authors":"Sunil Kumar Sunani, Rubin Debbarma, Bishnu Maya Bashyal, S. Raghu, B. Jeevan","doi":"10.1016/j.cropro.2024.107072","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107072","url":null,"abstract":"","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"68 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.cropro.2024.107076
Tomáš Zoubek, Roman Bumbálek, Jean de Dieu Marcel Ufitikirezi, Miroslav Strob, Martin Filip, František Špalek, Aleš Heřmánek, Petr Bartoš
In this study, we investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds. A comprehensive dataset was created, capturing diverse conditions and annotated for three target classes: radish, radish-line, and weed. Through extensive experimentation involving 39 combinations of model types, batch sizes (2, 4, 8), and learning rates (0.1, 0.01, 0.001), we determined that the YOLOv5-x model with a batch size of 4 and a learning rate of 0.01 offers superior performance. This configuration achieved a remarkable 99% accuracy for the radish class, 98% for radish-line, and 91% for weed, as confirmed by confusion matrices. Further analysis using the F1-score, Precision-Recall (PR) curves, and training progress plots underscored the model's robustness, particularly its high mAP_0.5:0.95 score. Despite the Weed class posing greater detection challenges, likely due to its lower representation in the dataset, the YOLOv5-x outperformed YOLOR-D6 and YOLOv7-D6 in critical metrics after 300 epochs. This research not only highlights the efficacy of YOLOv5-x in agricultural applications but also suggests potential enhancements in data annotation and model training strategies to further improve weed detection. Our findings provide significant insights for developing automated, high-precision plant-weed detection systems, contributing to more efficient and sustainable agricultural practices.
{"title":"Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition","authors":"Tomáš Zoubek, Roman Bumbálek, Jean de Dieu Marcel Ufitikirezi, Miroslav Strob, Martin Filip, František Špalek, Aleš Heřmánek, Petr Bartoš","doi":"10.1016/j.cropro.2024.107076","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107076","url":null,"abstract":"In this study, we investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds. A comprehensive dataset was created, capturing diverse conditions and annotated for three target classes: radish, radish-line, and weed. Through extensive experimentation involving 39 combinations of model types, batch sizes (2, 4, 8), and learning rates (0.1, 0.01, 0.001), we determined that the YOLOv5-x model with a batch size of 4 and a learning rate of 0.01 offers superior performance. This configuration achieved a remarkable 99% accuracy for the radish class, 98% for radish-line, and 91% for weed, as confirmed by confusion matrices. Further analysis using the F1-score, Precision-Recall (PR) curves, and training progress plots underscored the model's robustness, particularly its high mAP_0.5:0.95 score. Despite the Weed class posing greater detection challenges, likely due to its lower representation in the dataset, the YOLOv5-x outperformed YOLOR-D6 and YOLOv7-D6 in critical metrics after 300 epochs. This research not only highlights the efficacy of YOLOv5-x in agricultural applications but also suggests potential enhancements in data annotation and model training strategies to further improve weed detection. Our findings provide significant insights for developing automated, high-precision plant-weed detection systems, contributing to more efficient and sustainable agricultural practices.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"89 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/j.cropro.2024.107069
Dasen Li, Zhendong Yin, Yanlong Zhao, Yaqin Zhao, Hongjun Zhang
Deep learning has been widely applied as a general technique for image classification in plant diagnosis. Despite the impressive performance verified by individual classification tasks, deep learning networks suffer from forgetting the knowledge of old-type when updating the input stream by new disease samples in the continual plant diagnosis. Recently, rehearsal-based class-incremental learning approaches for plant disease classification have been proposed to mitigate the effects of old-type forgetting. These methods stored parts of leaf images of old disease, then replayed old exemplars and trained jointly with the new disease data in a class-incremental task. However, privacy issues and a considerable amount of memory limit the application of these rehearsal-based methods. In this paper, we investigate non-exemplar class-incremental learning schemes for plant diagnosis to address the catastrophic forgetting problem without requiring extra memory space for stored exemplars. We introduce a new non-exemplar class-incremental learning scheme, NeCILPD, for continual plant diagnosis. In particular, we propose an improved self-supervision learning algorithm and a novel prototype inversion constraint strategy to mitigate the problem of prototype shifts, in order to further improve the performance of few-shot class-incremental learning tasks. Experimental results confirmed the effectiveness of the proposed class-incremental learning approach. Specifically, the proposed class-incremental learning scheme achieved 70.27% accuracy and 17.80% forgetting measure in the incremental classification of 30 categories, outperforming the current SOTA method, which attained 63.80% accuracy and a forgetting measure of 24.80%. The impressive performance of the proposed non-exemplar class-incremental learning scheme provides a reliable tool for continual plant diagnosis, laying a solid foundation for agricultural applications.
{"title":"Non-exemplar class-incremental learning for continual plant diagnosis","authors":"Dasen Li, Zhendong Yin, Yanlong Zhao, Yaqin Zhao, Hongjun Zhang","doi":"10.1016/j.cropro.2024.107069","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107069","url":null,"abstract":"Deep learning has been widely applied as a general technique for image classification in plant diagnosis. Despite the impressive performance verified by individual classification tasks, deep learning networks suffer from forgetting the knowledge of old-type when updating the input stream by new disease samples in the continual plant diagnosis. Recently, rehearsal-based class-incremental learning approaches for plant disease classification have been proposed to mitigate the effects of old-type forgetting. These methods stored parts of leaf images of old disease, then replayed old exemplars and trained jointly with the new disease data in a class-incremental task. However, privacy issues and a considerable amount of memory limit the application of these rehearsal-based methods. In this paper, we investigate non-exemplar class-incremental learning schemes for plant diagnosis to address the catastrophic forgetting problem without requiring extra memory space for stored exemplars. We introduce a new non-exemplar class-incremental learning scheme, NeCILPD, for continual plant diagnosis. In particular, we propose an improved self-supervision learning algorithm and a novel prototype inversion constraint strategy to mitigate the problem of prototype shifts, in order to further improve the performance of few-shot class-incremental learning tasks. Experimental results confirmed the effectiveness of the proposed class-incremental learning approach. Specifically, the proposed class-incremental learning scheme achieved 70.27% accuracy and 17.80% forgetting measure in the incremental classification of 30 categories, outperforming the current SOTA method, which attained 63.80% accuracy and a forgetting measure of 24.80%. The impressive performance of the proposed non-exemplar class-incremental learning scheme provides a reliable tool for continual plant diagnosis, laying a solid foundation for agricultural applications.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"91 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weeds prevent the growth of the lentil crop, which significantly reduces yield. Imazethapyr (IM), a post-emergence herbicide, primarily inhibited acetolactate synthase activity by reducing branched-chain amino acid contents which had an impact on plant growth. IM application has a secondary impact of increasing oxidative stress due to ALS inhibition. The objective of the present study was to elucidate the role of antioxidant defence mechanisms in mitigating oxidative stress induced by IM treatment and weed interference in IM-tolerant (LL1397 & LL1612) and susceptible (FLIP2004-7 L & PL07) lentil genotypes that were grown under control, weedy check, and IM-treated conditions. Lower H2O2 and MDA content in tolerant genotypes after IM spray were due to increased NOX (NADPH oxidase), SOD (superoxide dismutase), CAT (catalase), peroxidase (POX), GR (glutathione reductase), APX (ascorbate peroxidase), MDHAR (monodehydroascorbate reductase) and DHAR (dehydroascorbate reductase) along with higher ascorbate (AsA) and lower DHA (dehydroascorbate) content, helped plants recover from herbicide stress. However, in susceptible genotypes, higher H2O2 and MDA contents due to reduced CAT, APX, MDHAR, DHAR and NOX activities led to loss of membrane integrity by lipid peroxidation that adversely affected the plant growth. Increased SOD, GR, MDHAR, and DHAR activities in tolerant genotypes under weedy check treatment tried to cope with weed interference-induced oxidative stress, but a lower magnitude of increase in enzyme activities and decreased H2O2-detoxifying enzyme activities led to incomplete detoxification of ROS, which affected plant development. This is the first in-depth investigation of the IM tolerance mechanism in lentil. The identified tolerance mechanism during herbicide stress and weed interference in lentil will be useful in integrated herbicide weed management.
杂草阻碍了扁豆作物的生长，这大大降低了产量。Imazethapyr （IM）是一种萌发后除草剂，主要通过降低支链氨基酸含量来抑制乙酰乳酸合成酶活性，从而影响植物的生长。由于ALS抑制，IM的应用具有增加氧化应激的次要影响。本研究的目的是阐明抗氧化防御机制在缓解IM处理和杂草干扰诱导的耐IM (LL1397 &；LL1612)和敏感(FLIP2004-7 L &；在控制、杂草检查和im处理条件下生长的PL07)小扁豆基因型。耐药基因型在喷施IM后，由于NOX （NADPH氧化酶）、SOD（超氧化物歧化酶）、CAT（过氧化氢酶）、过氧化物酶（POX）、GR（谷胱甘肽还原酶）、APX（抗坏血酸过氧化物酶）、MDHAR（单脱氢抗坏血酸还原酶）和DHAR（脱氢抗坏血酸还原酶）含量增加，同时抗坏血酸（AsA）和DHA（脱氢抗坏血酸）含量增加，H2O2和MDA含量降低，有助于植物从除草剂胁迫中恢复。然而，在敏感基因型中，由于CAT、APX、MDHAR、DHAR和NOX活性降低，H2O2和MDA含量升高，导致脂质过氧化作用导致膜完整性丧失，对植物生长产生不利影响。杂草抑制处理下，抗性基因型的SOD、GR、MDHAR和DHAR活性升高，试图应对杂草干扰诱导的氧化胁迫，但酶活性升高幅度较低，h2o2解毒酶活性降低，导致活性氧脱毒不完全，影响植株发育。这是第一次对小扁豆耐微生物机制的深入研究。确定了小扁豆对除草剂胁迫和杂草干扰的耐受机制，为小扁豆除草剂杂草综合治理提供了理论依据。
{"title":"Imazethapyr and weed interference-induced oxidative stress change the dynamics of the antioxidant defence system in lentil (Lens culinaris Medik.)","authors":"Shivani, Satvir Kaur Grewal, Ranjit Kaur Gill, Harpreet Kaur Virk, Rachana D. Bhardwaj","doi":"10.1016/j.cropro.2024.107068","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107068","url":null,"abstract":"Weeds prevent the growth of the lentil crop, which significantly reduces yield. Imazethapyr (IM), a post-emergence herbicide, primarily inhibited acetolactate synthase activity by reducing branched-chain amino acid contents which had an impact on plant growth. IM application has a secondary impact of increasing oxidative stress due to ALS inhibition. The objective of the present study was to elucidate the role of antioxidant defence mechanisms in mitigating oxidative stress induced by IM treatment and weed interference in IM-tolerant (LL1397 & LL1612) and susceptible (FLIP2004-7 L & PL07) lentil genotypes that were grown under control, weedy check, and IM-treated conditions. Lower H<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">2</ce:inf> and MDA content in tolerant genotypes after IM spray were due to increased NOX (NADPH oxidase), SOD (superoxide dismutase), CAT (catalase), peroxidase (POX), GR (glutathione reductase), APX (ascorbate peroxidase), MDHAR (monodehydroascorbate reductase) and DHAR (dehydroascorbate reductase) along with higher ascorbate (AsA) and lower DHA (dehydroascorbate) content, helped plants recover from herbicide stress. However, in susceptible genotypes, higher H<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">2</ce:inf> and MDA contents due to reduced CAT, APX, MDHAR, DHAR and NOX activities led to loss of membrane integrity by lipid peroxidation that adversely affected the plant growth. Increased SOD, GR, MDHAR, and DHAR activities in tolerant genotypes under weedy check treatment tried to cope with weed interference-induced oxidative stress, but a lower magnitude of increase in enzyme activities and decreased H<ce:inf loc=\"post\">2</ce:inf>O<ce:inf loc=\"post\">2</ce:inf>-detoxifying enzyme activities led to incomplete detoxification of ROS, which affected plant development. This is the first in-depth investigation of the IM tolerance mechanism in lentil. The identified tolerance mechanism during herbicide stress and weed interference in lentil will be useful in integrated herbicide weed management.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"13 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/j.cropro.2024.107070
Yuan-Cheng Hsu, Chao-Jen Wang, Wen-Hsin Chung
In 2019, an unknown wilting disorder on snow bush (Breynia disticha) was observed in Changhua County, Taiwan. Symptomatic snow bush plants exhibited wilting, necrotic, and vascular discoloration. Two strains Bre-RS1 and Bre-RS2 were isolated from symptomatic tissues. Based on physiological tests and molecular analysis, the two bacterial strains were identified as Ralstonia pseudosolanacearum biovar 4. The pathogenicity tests showed that two isolates belonging to race 1 could infect Phyllanthaceae, Solanaceae, and Convolvulaceae with wound inoculation. This report describes a new bacterial wilt disease of snow bush caused by R. pseudosolanacearum in Taiwan and the world.
{"title":"First report of snow bush wilt caused by Ralstonia pseudosolanacearum (Ralstonia solanacearum phylotype I) in Taiwan","authors":"Yuan-Cheng Hsu, Chao-Jen Wang, Wen-Hsin Chung","doi":"10.1016/j.cropro.2024.107070","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107070","url":null,"abstract":"In 2019, an unknown wilting disorder on snow bush (<ce:italic>Breynia disticha</ce:italic>) was observed in Changhua County, Taiwan. Symptomatic snow bush plants exhibited wilting, necrotic, and vascular discoloration. Two strains Bre-RS1 and Bre-RS2 were isolated from symptomatic tissues. Based on physiological tests and molecular analysis, the two bacterial strains were identified as <ce:italic>Ralstonia pseudosolanacearum</ce:italic> biovar 4. The pathogenicity tests showed that two isolates belonging to race 1 could infect Phyllanthaceae, Solanaceae, and Convolvulaceae with wound inoculation. This report describes a new bacterial wilt disease of snow bush caused by <ce:italic>R</ce:italic>. <ce:italic>pseudosolanacearum</ce:italic> in Taiwan and the world.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"24 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1016/j.cropro.2024.107058
Akshay Dheeraj, Satish Chand
The agricultural crops, like corn, suffer from the presence of undesirable plants known as weeds, which compete for sunlight and water, leading to lower crop yields. Recognizing weeds during their early growth stage is vital for minimizing their impact on crop growth and maximizing yield. By leveraging a lightweight deep neural network, this research endeavours to classify corn and the weeds that often grow alongside it. To achieve this, the Enhanced Convolutional Block Attention Module (CBAM) embedded EfficientNet model (ECENet) is proposed by integrating the enhanced CBAM with EfficientNetB0 model and the inclusion of extra layers. The Enhanced CBAM has been created by modifying the original CBAM through the parallel arrangement of the Channel Attention Module (CAM) and Spatial Attention Module (SAM). The simultaneous use of attention modules eradicates the need for CAM and SAM to be dependent on each other, resulting in the independent extraction of attention feature maps. The ECENet model was trained and tested on the corn weed dataset to understand the discriminative features of corn and weed. The proposed system yielded 99.92% overall recognition accuracy, with 4,772,010 parameter footprints, a model size of 57.4 megabytes, and 0.796 giga floating-point operations per second (GFLOPs). The proposed ECENet takes 37%, 91%, 80%, and 78% fewer parameters than DenseNet121, InceptionResNetV2, ResNet50V2, and XceptionNet respectively. The proposed model excels in diagnosing weed and crop differentiation, outperforming previous studies and state-of-the-art models. Finally, interpretability of the proposed model has been provided using explainable AI techniques (XAI) such as GradCAM and LIME. Due to its small memory requirement and high accuracy, the ECENet is ideal for real-time corn and weed classification on handy and mobile devices with minimal computational capabilities. The system can also be expanded to be included in agricultural robots for real-world weeding in large farmlands.
{"title":"Deep learning based weed classification in corn using improved attention mechanism empowered by Explainable AI techniques","authors":"Akshay Dheeraj, Satish Chand","doi":"10.1016/j.cropro.2024.107058","DOIUrl":"https://doi.org/10.1016/j.cropro.2024.107058","url":null,"abstract":"The agricultural crops, like corn, suffer from the presence of undesirable plants known as weeds, which compete for sunlight and water, leading to lower crop yields. Recognizing weeds during their early growth stage is vital for minimizing their impact on crop growth and maximizing yield. By leveraging a lightweight deep neural network, this research endeavours to classify corn and the weeds that often grow alongside it. To achieve this, the Enhanced Convolutional Block Attention Module (CBAM) embedded EfficientNet model (ECENet) is proposed by integrating the enhanced CBAM with EfficientNetB0 model and the inclusion of extra layers. The Enhanced CBAM has been created by modifying the original CBAM through the parallel arrangement of the Channel Attention Module (CAM) and Spatial Attention Module (SAM). The simultaneous use of attention modules eradicates the need for CAM and SAM to be dependent on each other, resulting in the independent extraction of attention feature maps. The ECENet model was trained and tested on the corn weed dataset to understand the discriminative features of corn and weed. The proposed system yielded 99.92% overall recognition accuracy, with 4,772,010 parameter footprints, a model size of 57.4 megabytes, and 0.796 giga floating-point operations per second (GFLOPs). The proposed ECENet takes 37%, 91%, 80%, and 78% fewer parameters than DenseNet121, InceptionResNetV2, ResNet50V2, and XceptionNet respectively. The proposed model excels in diagnosing weed and crop differentiation, outperforming previous studies and state-of-the-art models. Finally, interpretability of the proposed model has been provided using explainable AI techniques (XAI) such as GradCAM and LIME. Due to its small memory requirement and high accuracy, the ECENet is ideal for real-time corn and weed classification on handy and mobile devices with minimal computational capabilities. The system can also be expanded to be included in agricultural robots for real-world weeding in large farmlands.","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"96 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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