Advanced biosensing technologies for monitoring of agriculture pests and diseases:A review

The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security.Early detection is of critical importance to make accurate predictions,optimize control strategies and prevent crop losses.Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases.Biosensing methodologies offer potential solutions for real-time and automated monitoring,which allow advancements in early and accurate detection and thus support sustainable crop protection.Herein,advanced biosensing technologies for pests and diseases monitoring,including image-based technologies,electronic noses,and wearable sensing methods are presented.Besides,challenges and future perspectives for widespread adoption of these technologies are discussed.Moreover,we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration,which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

Resequencing-based QTL mapping for yield and resistance traits reveals great potential of Oryza longistaminata in rice breeding

As a natural genetic reservoir, wild rice contains many favorable alleles and mutations conferring high yield and resistance to biotic and abiotic stresses. However, there are few reports describing favorable genes or QTL from the AA genome wild rice O. longistaminata, which is characterized by tall and robust habit and long tassels and anthers and shows high potential for use in cultivated rice improvement. We constructed a stable BC_(2)F_(20) backcross inbred line(BIL) population of 152 lines from the cross of 9311 × O.longistaminat. Some BILs showed large panicles, large seeds, and strong resistance to rice false smut, bacterial leaf blight, rice blast spot, and brown planthopper. Genomic resequencing showed that the 152 BILs covered about 99.6% of the O. longistaminata genome. QTL mapping with 2432 bin markers revealed 13QTL associated with seven yield traits and eight with resistance to brown planthopper and to four diseases. Of these QTL, 12 for grain yield and 11 for pest and disease resistance are novel in Oryza species.A large-panicle NIL1880 line containing QTL qPB8.1 showed a nearly 50% increase in spikelet number and27.5% in grain yield compared to the recurrent parent 9311. These findings support the potential value of O. longistaminata for cultivated rice improvement.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

Experimental study on spraying performance of biological pesticides in aerial rotary cage nozzle

Aviation spraying is one of the most important methods for the treatment of forestry pests and diseases,which has a high operation efficiency and effectiveness.The combination of aviation spray and biological pesticides to reduce the impact of pesticide spray on environment has become an important development in the direction of agricultural aviation.In order to explore the effects of aviation spray parameters on particle size distribution and activity retention of biological pesticide droplets,a system of droplets size test and an activity retention rate test system of biological pesticides were constructed.The related experiments were conducted by changing the structural parameters of the rotary cage nozzle(the diameter of nozzle)and the technical parameters of the operation(rotary speed and flow rate).The results showed that the average relative width S of the droplets of the rotary cage nozzle was close to 1,and the droplet distribution was uniform.The influence factors on the particle size distribution of biological pesticide droplets were rotary speed,nozzle diameter,medium and flow rate in descending order.The rotary speed,nozzle diameter,and medium had a significant influence on droplet size distribution.The average activity retention rate of Bacillus thuringiensis was 88.83%,and the germination rate of Beauveria bassiana was 89.80%.The rotary speed had a significant effect on the activity retention rate of bacterial and fungal biological pesticides,and it was negatively correlated.The rotary speed was between 3000-5000 r/min,and the activity retention rate was higher.The research results can provide guidance for spraying biological pesticides in aerial plant protection operations with rotary cage nozzle.