Search Results (6)

We evaluated the effectiveness of three different treatment groups at managing apple shoot blight, and the resulting canker incidence and canker length on wood caused by Erwinia amylovora. Preventative foliar sprays or trunk injections of giant knotweed extract (Regalia), oxytetracycline (Arbor-OTC or FireLine + Regulaid), or streptomycin (Agri-mycin/FireWall + Regulaid) were applied to mature ‘Fuji’ trees. Regalia and oxytetracycline were ineffective at reducing shoot blight severity, showing poor disease reductions of 18.2% and 24.3% compared to untreated controls across both years. Streptomycin was effective at controlling shoot blight severity when applied as a spray application, reducing necrosis by up to 93.9% across both years. Canker incidence was also poorly reduced by Regalia and oxytetracycline with an average decrease of 33.3% and 52.4%, respectively. Again, spray applications of streptomycin were most effective at reducing canker incidence (95.2%). When present, canker length was best controlled by spray applications of streptomycin, showing an average reduction of 95.7%. The effectiveness of Regalia and oxytetracycline was poor, reducing canker length by only 30.4% and 43.5%, respectively. Trunk injections of Regalia were consistently less effective than spray applications. Compared to their spray application counterpart, Regalia injections were, on average, 12.5%, 26.3%, and 25.1% less effective at reducing shoot blight severity, canker incidence, and canker length, respectively. Injected Arbor-OTC was more effective than spray applications of oxytetracycline. On average, Arbor-OTC injections were up to 28.3%, 40.1%, and 30% more effective at reducing shoot blight severity, canker incidence, and canker length compared to spray applications. Overall, Regalia and oxytetracycline were not as effective as streptomycin at controlling fire blight. The search for organic antibiotic alternatives for shoot blight and canker control continues, as cankers are increasing in economic importance by causing bearing wood and young tree death. Full article

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8. Full article

A two-year trial (2021 and 2022) was performed with five different fungicide and foliar fertiliser application strategies to control apple scab (Venturia inaequalis) in integrated apple orchards in Latvia. A strategy of using inorganic fungicides or combining them with synthetic fungicides was compared to a strategy of applications with only synthetic fungicides and untreated control. Furthermore, two strategies included foliar fertilisers to determine whether they may affect apple scab used alone or combined with synthetic fungicides. The timing of the fungicide applications was based on the risk forecasted by the decision support system RIMpro, and fertilisers were used at certain growth stages of the crop. The disease incidence on untreated fruits on cv. Auksis ranged from 38.3% to 59.6%, and on cv. Ligol from 99.3% to 99.5%. Strategies including synthetic fungicides were the most effective against scab on shoot leaves and fruits. The strategy using only inorganic fungicides was effective for low-inoculum orchards. Combining synthetic and inorganic fungicides provided the best apple scab control strategy on fruits, likely helping reduce the resistance selection pressure and residues of synthetic fungicides. Foliar fertilisers were insufficient to control apple scab; they would supplement existing scab fungicide programs. Full article

Apple foliar diseases are a group of diseases that affect the leaves of apple trees. These diseases can significantly impact apple tree health and fruit yield. Ordinary apple foliar diseases include frog_eye_leaf_spots, powdery mildew, rust, apple scabs, etc. Early detection of these diseases is important for effective apple crop management to increase the yield of apples. Therefore, this research proposes a fine-tuned EfficientNetB3 model for the quick and precise assessment of these apple foliar diseases. A dataset containing 23,187 RGB images of eleven different apple foliar diseases is used for experimentation. The proposed model is compared with four transfer learning models, i.e., InceptionResNetV2, ResNet50, AlexNet, and VGG16. All models are fine-tuned by adding different layers like the global average pooling layer, flatten layer, dropout layer, and dense layer. The performance of these five models is compared in terms of the precision, recall, accuracy, and F1-score. The EfficientNetB3 outperformed the other models in terms of all performance parameters. The best model is further optimized with the help of three optimizers, i.e., Adam, SGD, and Adagrad. The proposed model achieved the precision, recall, and F1-score values of 86%, 88%, and 86%, respectively, at 32 batch sizes and 10 epochs. This research formulated a model for an apple foliar disease diagnosis within sustainable agriculture. Full article

In many parts of the world, apple trees suffer from severe foliar damage each year due to infection of Alternaria blotch (Alternaria alternata f. sp. Mali), resulting in serious economic losses to growers. Traditional methods for disease detection and severity classification mostly rely on manual labor, which is slow, labor-intensive and highly subjective. There is an urgent need to develop an effective protocol to rapidly and accurately evaluate disease severity. In this study, DeeplabV3+, PSPNet and UNet were used to assess the severity of apple Alternaria leaf blotch. For identifications of leaves and disease areas, the dataset with a total of 5382 samples was randomly split into 74% (4004 samples) for model training, 9% (494 samples) for validation, 8% (444 samples) for testing and 8% (440 samples) for overall testing. Apple leaves were first segmented from complex backgrounds using the deep-learning algorithms with different backbones. Then, the recognition of disease areas was performed on the segmented leaves. The results showed that the PSPNet model with MobileNetV2 backbone exhibited the highest performance in leaf segmentation, with precision, recall and MIoU values of 99.15%, 99.26% and 98.42%, respectively. The UNet model with VGG backbone performed the best in disease-area prediction, with a precision of 95.84%, a recall of 95.54% and a MIoU value of 92.05%. The ratio of disease area to leaf area was calculated to assess the disease severity. The results showed that the average accuracy for severity classification was 96.41%. Moreover, both the correlation coefficient and the consistency correlation coefficient were 0.992, indicating a high agreement between the reference values and the value that the research predicted. This study proves the feasibility of rapid estimation of the severity of apple Alternaria leaf blotch, which will provide technical support for precise application of pesticides. Full article

Organic apple production in the eastern US is small and is mostly based on existing varieties, which are susceptible to scab, and rootstocks, which are susceptible to fire blight. This requires numerous sprays per year of various pesticides to produce acceptable fruit. From 2014 to 2016, we tested different arthropod, disease and weed management programs in an advanced tall spindle high-density production system that included disease-resistant cultivars and rootstocks, in an organic research planting of apples in Geneva, New York. Arthropod and disease management regimens were characterized as Advanced Organic, Minimal Organic, or Untreated Control. Results varied by year and variety, but, in general, the Advanced program was more effective than the Minimal program in preventing damage from internal-feeding Lepidoptera, plum curculio, and obliquebanded leafroller, and less effective than the Minimal program against damage by foliar insects. Both organic programs provided comparable control of sooty blotch, cedar apple rust, and fire blight, with some variability across cultivars and years. The advanced selection CC1009 and Modi seemed to possess complete resistance to cedar apple rust, while Pristine had partial resistance. For weed control, bark chip mulch, organic soap sprays, and limonene sprays tended to be most effective, while mechanical tillage and flame weeding had lower success. Full article