Plant growth promoting microbial consortia against late blight disease of tomato under natural epiphytotic conditions

Singh, Raghuveer; Ao, N. Tiameren; Kangjam, Valenta; Rajesha, G.; Banik, Susanta

doi:10.1007/s42360-022-00464-1

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…The main pest species are Aphid [8] , Helicoverpa Armigera [9] , Spider Mite [10] and White Fly [11] , all of them are highly reproductive, fast-growing and widespread when the environment is suitable, and in addition to direct damage, can also spread directly or promote secondary infection of the disease [12] . According to statistics, approximately 15% of global tomato production is affected by pests and diseases each year, with average yield reductions in severe regions capable of reaching 40% to 80% [13,14] . Careful control of pests and diseases is a key task to reduce losses and increase crop yields.…”

Section: Introductionmentioning

confidence: 99%

Online diagnosis platform for tomato seedling diseases in greenhouse production

Jin,

Zhu,

et al. 2024

International Journal of Agricultural and Biological Engineering

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The facility-based production method is an important stage in the development of modern agriculture, lifting natural light and temperature restrictions and helping to improve agricultural production efficiency. To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities, an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment. The diagnostic platform consists of three main parts: pest and disease information detection, clustering and decision-making of detection results, and platform diagnostic display. Firstly, based on the You Only Look Once (YOLO) algorithm, the key information of the disease was extracted by adding attention module (CBAM), multi-scale feature fusion was performed using weighted bidirectional feature pyramid network (BiFPN), and the overall construction was designed to be compressed and lightweight; Secondly, the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications; Finally, a detection platform was designed and developed using Python, including the front-end, back-end, and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases. The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP (mean Average Precision) of 92.7%, weights of 12.8 Megabyte (M), inference time of 33.6 ms. Compared with the current mainstream single-stage detection series algorithms, the improved algorithm model has achieved better performance; The accuracy rate of the platform diagnosis output pests and diseases information of 91.2% for images and 95.2% for videos. It is a great significance to tomato pest control research and the development of smart agriculture.

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Section: Introductionmentioning

confidence: 99%

Online diagnosis platform for tomato seedling diseases in greenhouse production

Jin,

Zhu,

et al. 2024

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…According to statistics, approximately 15% of global tomato production is affected by pests and diseases each year, with average yield reductions in severe regions capable of reaching 40-80% [13,14]. Careful control of pests and diseases is a key task to reduce losses and increase crop yields.…”

Section: Introductionmentioning

confidence: 99%

Design and Research of an Online Diagnosis Platform for Tomato Seedling Facilities Production Diseases

Jin

Zhu

et al. 2023

Preprint

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The facility-based production method is an important stage in the development of modern agriculture, lifting natural light and temperature restrictions and helping to improve agricultural production efficiency. To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities, an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment. The diagnostic platform consists of three main parts: pest and disease information detection, clustering and decision making of detection results, and platform diagnostic display. Firstly, based on the YOLOv5 algorithm, the key information of the disease was extracted by adding attention module (CBAM), multi-scale feature fusion was performed using weighted bi-directional feature pyramid network (BiFPN), and the overall construction was designed to be compressed and lightweight; Secondly, a K-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications; Finally, a web-side pest detection platform was designed and developed using Python, including the front-end, back-end, and database of the system to realize online diagnosis and interaction of tomato plant diseases and pests. The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP of 92.7% (mAP@0.5), weights of 12.8M, inference time of 33.6ms. Compared with the current mainstream single-stage detection series algorithms, the improved algorithm model has achieved better performance; The accuracy rate of the platform diagnosis output disease information of tomato images is 91.2%, and the accuracy rate of video disease information is 95.2%.

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