Pest and disease incidence of coniferous species in Taman Saujana Hijau,Putrajaya urban park,Malaysia

Taman Saujana Hijau(TSH),Putrajaya is a 41-ha urban park planted with various coniferous species from around the world.Insect pests and disease incidences of this park are unknown and there is a need for an evaluation of the health status of this urban park.This study assessed the level of pest and disease incidents of coniferous species in 12 plots of 7 species(Araucaria bidwilii,Araucaria haterophylla,Araucaria cunninghamii,Pinus caribaea,Pinus merkusii,Podocarpus polystachyus,and Podocarpus costalis).Termites,canker disease,and foliar disease are three major problems.The highest pest and disease incidence(PnDI)was foliar disease with a 0.49 coefficient correlation between the total number of trees and the PnDI,followed by canker disease with 0.40,and termites with 0.36.Of the seven conifers,A.haterophylla was the most infected followed by A.bidwilii and A.cunninghamii.It was concluded that the incidence of pests and diseases in TSH was moderate.To our knowledge,this may be the first baseline inventory of pests and diseases of coniferous species in Malaysia.

Facility Cultivation Techniques of ‘Kaorino’ Strawberry

Strawberry‘Kaorino’is one of the perfect early-maturing strawberry varieties with high quality and high yield due to its anthracnose resistance,early maturity and good quality.The variety has become more and more popular with the promotion over the last few years,but there are great differences from other varieties in cultivation.Based on its varietal characteristics and cultivation performance in production over the last few years,we summarized the seedling cultivation techniques,planting management techniques,main disease control techniques and harvesting and storage techniques,aiming to provide reference for better promotion and application of‘Kaorino’.

Online diagnosis platform for tomato seedling diseases in greenhouse production

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 bi-directional 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.