Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques. | LitMetric
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
The use of pesticides for diseases and insect pest control has become a key component in smallholder vegetable production. This study therefore quantified the concentration of pesticide active ingredient per unit production land (kg a.i/ha), and drivers of increased pesticide use in smallholder vegetable production systems in Tanzania.
Induction of systemic acquired resistance (SAR) in plants to control bacterial diseases has become an effective solution to the problems of agrochemical resistance and ecological environment damage caused by long-term and large-scale use of traditional bactericides. However, current SAR-inducing compounds are often unable to rapidly eliminate pathogenic bacteria in infected plant tissues to prevent further spread of the disease, severely restraining the potential for extensive application in agriculture. Herein, we address the limitations by developing a series of visible-light-absorbing aggregation-induced emission photosensitizers suitable for agricultural use.
In Arabidopsis thaliana, the transcription factors WRKY7, WRKY11 and WRKY17 act as negative defence regulators against Pseudomonas syringae pv. tomato (Pst) DC3000. However, their coordinated regulation of gene expression has yet to be fully explored.
State Key Laboratory for Conservation and Utilization of Bioresources in Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming, 650201, China.
Tuta absoluta (Lepidoptera: Gelechiidae) is one of the most significant invasive and destructive pests worldwide, causing serious economic losses to the tomato industry. Rhizosphere microorganism, such as arbuscular mycorrhizal fungi (AMF) and Pseudomonas bacteria, can interact with plants individually or collectively to improve plant growth and resistance to pests and disease. However, the effects of AMF, Pseudomonas, and their interactions on plant responses to insect herbivores remain unclear.
During the field visits in growing season of 2022 in Dammam Region of Saudi Arabia, begomovirus-like symptoms including leaf curling, leaf cupping, leaf distortion, vein thickening and reduced leaf size were observed in squash and cucumber fields. Twenty-five samples were collected from each crop and PCR amplification was done using general diagnostic begomovirus primers (AC-1048/AV-494 and Begomo I/Begomo II). The obtained results showed desired sized amplified DNA fragments (550 bp and 1.
