31 results about "Pine forest" patented technology

The invention relates to the technical field of green prevention and treatment of dendroctonus valerieus, in particular to a dendroctonus valerieus repellent as well as a preparation method and application thereof. The repelling component comprises fenchone. The fenchone is found to have an obvious repelling effect on the dendroctonus resurus, and meanwhile, the fenchone can be used as a repellent for the dendroctonus resurus, and the dendroctonus resurus can be repelled in production to protect an ecological system of a pine forest. The repellent has the advantages of good repelling effect, stability, environmental friendliness and wide industrialization prospect.

The present disclosure provides a method for identifying a pine wood nematode-infected discolored wood in a mixed coniferous and broadleaf forest, including inputting a pine forest image of the mixed coniferous and broadleaf forest to be identified into a trained identification model for the pine wood nematode-infected discolored wood to identify the pine wood nematode-infected discolored wood in the mixed coniferous and broadleaf forest, wherein the trained identification model is improved based on a you Only Look Once version 5 small (YOLOv5s) model by connecting a feature-filtering module after a Neck; constructing a feature-enhancing module to replace a C3 module in a Backbone; constructing a convolution-transformer module based on multi-head self-attention (MSHA) to connect after a last layer of residual units in the Backbone; constructing, by Group Shuffle Convolution (GSConv), a multi-scale feature fusion layer to replace an ordinary convolution in the Neck.

The invention discloses a seedling breeding and under-forest cultivation method for Schefflera octophylla. Relates to the technical field of medicinal plant cultivation. Comprising the following steps: seed treatment; sowing and germinating; seedling cultivation; performing under-forest field planting; and managing after planting. The germination rate is high and neat; seedlings are robust and strong in stress resistance; the field planting survival rate is obviously improved, and the under-forest field planting survival rate is stabilized to be 85% or above; the whole technical route does not need to use chemical pesticides and chemical fertilizers, the Yunnan pine forest land resources are utilized, bionic cultivation of the stichopus japonicus is achieved, the ecological environment is protected, and technical support is provided for production of high-quality medicinal materials of the stichopus japonicus.

This invention discloses a method for establishing a spectral characteristic database of trees affected by wood nematode, involving the interdisciplinary fields of forestry disease monitoring, spectral analysis, and database technology. By capturing subtle spectral changes in pine needles in the red and near-infrared bands, this invention can identify signs of infection before obvious symptoms appear on the tree's surface. Through a multi-step, multi-index comprehensive analysis method, it effectively eliminates interference from other ground features such as forest land and bare soil. The established dedicated spectral model and indices are highly targeted, achieving higher classification and identification accuracy than using a single general index, significantly reducing the false positive rate. The use of UAV hyperspectral remote sensing technology overcomes the shortcomings of manual inspection, such as limited coverage, numerous blind spots, and poor timeliness, enabling rapid and comprehensive monitoring of large-scale pine forest resources. It can not only determine whether pine trees are infected but also quantitatively assess the severity of the disease through chlorophyll content estimation models and spectral characteristic differences.

A pine wilt disease tree detection method based on an attention mechanism and a center circle sampling method, which comprises the following steps: step 1, obtaining pine wilt disease tree pictures and making them into a data set; step 2, inputting the pine wilt disease tree data set into a global multi-scale channel adaptive network for training to obtain a trained pine wilt disease tree recognition model; step 3, inputting pine forest images that need to be recognized into the pine wilt disease tree recognition model to obtain the recognition result of the diseased tree and the latitude and longitude coordinates; and step 4, finding the diseased tree according to the coordinates of the diseased tree, and performing drug treatment on the early-stage diseased tree and cutting and burning treatment on the middle- and late-stage diseased tree. The purpose of the present application is to solve the technical problem that the existing pine wilt disease tree detection method ignores the mismatch between the positive sample sampling range and the circular shape of the pine wilt disease tree target, resulting in low sampling quality.

The invention relates to the field related to ecology and environment, and provides a water and soil conservation and carbon sequestration and sink increase improvement method for a Nanling mountain degraded agriculture and forestry composite system, which comprises the following steps: aiming at Nanling mountain degraded agriculture and forestry composite system treatment, fully considering the combination of engineering and vegetation measures, comprehensively planning ecological benefits and economic benefits, and improving the water and soil conservation and carbon sequestration and sink increase. Through the technical methods of'farmland rice straw square and slope melastoma dodecandrum vegetation covering ', 'fungus planting in pine forest land under forest and application of solid waste such as farmland rice hulls and straws to construction of fungus culture media' and the like, on one hand, land surface covering is increased, runoff is regulated and controlled, erosion is reduced, and the water and soil conservation function is enhanced; meanwhile, the treatment cost is reduced, the COemission is reduced, the carbon sink function of the system is enhanced, and the ecological benefits are improved. On the other hand, the resource utilization efficiency in the degeneration system is also improved, the product output of vegetation treatment measures is enhanced, the economic benefit is further improved, and finally the stability of the Nanling mountain degeneration agriculture and forestry composite ecological system is effectively improved.

The present invention discloses a Siamese Bacillus strain with oil-degrading capabilities and its applications. The strain, isolated from soil samples from a pine forest in Mount Tai and screened for oil-degrading activity, yielded Siamese Bacillus siamensis SM01-01, which is highly efficient in oil degradation. The strain is deposited with CGMCC No. 25550. Siamese Bacillus siamensis SM01-01 is capable of degrading oil in fish protein slurry. Under optimal conditions, the degradation rate reaches 89.76%, effectively resolving the issues of secondary fermentation and odor generation caused by residual oil in the fish protein slurry.

The invention discloses a forest stand structure regulation and control method for improving the carbon sequestration capacity of a pine forest, and particularly relates to the technical field of forestry ecological carbon sink regulation and control. Obtaining forest stand structure parameters and soil fertility parameters, and constructing a structure vector and a soil fertility vector; inputting the model into a carbon sink influence factor response model, and extracting a sensitive structure factor set C1; identifying grid units which do not meet a critical value based on C1, and delimiting a carbon sink structure bottleneck block P; performing regulation and control measures such as directional thinning, multi-layer building and structure intermediate cutting on the forest stand in the P block; dynamic evaluation is carried out through the change delta C of the carbon sink increase amount, and if the standard is not reached, the model weight is updated to form closed-loop optimization; the method realizes intelligent identification and accurate improvement of the pine forest carbon sequestration capability, and has the advantages of high operability, wide adaptability, remarkable carbon sequestration effect and the like.

The invention relates to the technical field of remote sensing monitoring, in particular to a pine wood nematode disease identification method and system based on an unmanned aerial vehicle image, and the method comprises the following steps: obtaining a pine forest multispectral image through an unmanned aerial vehicle, extracting edge pixels through a Canny algorithm, decomposing a direction to generate a sequence, calculating a gradient offset, counting curvature mutation points, and extracting curvature characteristic parameters. The method comprises the following steps: removing abnormal edge segments, performing DBSCAN clustering to obtain candidate areas, constructing a radial channel to collect gray values, generating gray attenuation features, extracting a three-dimensional feature space, and performing K-means clustering to output a recognition result graph. According to the method, multi-spectral image gradient decomposition is combined with curvature fluctuation characteristic analysis, disease spot microscopic distortion is accurately captured, non-pathological interference is inhibited through curvature mutation points and density clustering, three-dimensional characteristics are constructed through a radial gray attenuation main trend, gray identification is enhanced through direction offset analysis, experience dependence is reduced through multi-threshold screening, and the method is suitable for large-scale popularization and application. The dynamic self-matching framework improves the robustness of forest region detection, and realizes the autonomous recognition of disease spots.

The application discloses a kind of for decomposing forest litter composite microbial inoculant, preparation method and its application method, it is related to litter decomposition technical field.The application includes a kind of for decomposing forest litter composite microbial inoculant preparation method, comprising the following steps: S1.to Phanerochaete chrysosporium, Trichoderma reesei and ropegillus catenulatum are respectively carried out directional metabolism preactivation;S2.the three preactivation wet bacteria bodies obtained in step S1 are mixed according to the fresh weight ratio of Phanerochaete chrysosporium:Trichoderma reesei:ropegillus catenulatum 3:2:1, obtain mixed bacteria body;S3.mixed bacteria body is mixed with the straw carrier that is preprocessed by steam explosion according to the mass ratio of bacteria body fresh weight:carrier dry weight 1:2, under the condition of temperature 28 DEG C, relative humidity 92~95%, stationary culture 72 hours, carry out phased micro-perturbation aeration, construct physical symbiosis;S4.the material obtained in step S3 is dried to 15% moisture content at 40 DEG C, obtain composite microbial inoculant;Efficient and stable decomposition of pine forest litter is realized.

This invention discloses a method for collecting and converting remote sensing coordinate information of dead trees infected with pine wilt disease, belonging to the field of information conversion technology. The method comprises the following steps: Step 1: Accessing a pine forest data management database; Step 2: Identifying dead trees based on remote sensing images; Step 3: Coordinate matching; Step 4: Visual marking; and Step 5: Coordinate information conversion: Based on the coordinate information and the pine forest data management database, the remote sensing coordinate information is converted into prevention and control task information. By identifying dead trees based on remote sensing images and converting the coordinate information, the present invention accurately locates and identifies dead trees infected with pine wilt disease, thereby achieving efficient and comprehensive dead tree information collection, effectively addressing the inefficiency and omissions of manual inspections in existing technologies.

The invention relates to the technical field of Chinese herbal medicine planting, in particular to an under-forest ecological seedling raising method for Angelica sinensis, which comprises the following steps: land selection: selecting sparse pine forest land with the altitude of 1800-2500m and the canopy density of 0.3-0.5, and selecting a semi-sunny slope or shady slope with the annual rainfall of 800-1100mm, the annual average temperature of 12-18 DEG C and convenient water source; fertilizer preparation: preparing a first ecological fertilizer and a second ecological fertilizer; the first ecological fertilizer is obtained by mixing base materials according to a preset proportion and then performing composting treatment; the base material comprises pine needles, oak leaves, fern, sheep manure barnyard manure and an organic matter decomposition agent; the second ecological fertilizer is obtained by mixing and fermenting fresh sheep manure, brown sugar and water; seed pretreatment; soil preparation; sowing the seeds; building a shed; seedling management. The seedlings cultivated by the method are healthy, the disease carrying rate of soil-borne diseases such as root rot and nematodes is low, the weeding and water and fertilizer management cost is low, and the method is environment-friendly.

The invention discloses a pine forest yellowing reason determination method and device, a storage medium and electronic equipment, and the method is applied to the field of artificial intelligence, and comprises the steps: collecting image data of a pine forest region, and detecting whether there is a pine forest yellowing target region in the pine forest region or not according to the image data; under the condition that the target area is detected, whether the pine forest yellowing process of the target area belongs to a first type change or a second type change is judged according to the image data of the target area within a preset time period; obtaining the growth condition of the pine forest area, and calculating the confidence of the first type of reasons or the second type of reasons according to the rule base and the growth condition; and according to the confidence of the first type of reasons or the confidence of the second type of reasons, determining the reasons for yellowing of the pine forest in the target area. Through the method and the device, the problem that the pine forest yellowing reason obtained through analysis is inaccurate due to the fact that the pine forest yellowing reason can only be analyzed according to a single reason when the image data is analyzed through an artificial intelligence algorithm in related technologies is solved.

The invention discloses an under-forest ecological poria cocos planting method, and belongs to the technical field of under-forest poria cocos planting, and the specific planting method comprises the following steps: (1) poria cocos field preparation: selecting a pine forest suitable for poria cocos growth as a poria cocos field, and ploughing and cleaning sundries; (2) planting and inoculating: digging out a planting groove in a poria cocos field, putting cut-logs into the planting groove, inoculating poria cocos strains, and covering with soil mixed with temperature-regulating and growth-promoting particles; 4, planting management, wherein ditching and drainage are conducted, and hilling is conducted in time according to the poria cocos growth condition; (5) harvesting: after the poria cocos is mature, digging and harvesting the poria cocos in sequence. The yield and quality of the poria cocos are improved, and the problem that the poria cocos cannot be continuously cropped is solved.

The invention provides a pine wood nematode disease identification method and system in a low-light environment, and belongs to the field of forestry pest and disease monitoring. The method comprises the following steps: acquiring a pine forest aerial image to be detected, and preprocessing the image; processing the preprocessed image by adopting an image enhancement method based on CLIP to obtain an enhanced image; the enhanced image is input into a trained pine wood nematode disease detection model, and feature map extraction is carried out on the enhanced image through an OfficientViT module; performing up-sampling on the feature map by adopting content awareness feature recombination to obtain an enhanced feature map; and inputting the enhanced feature map into the RT-DETR network, and outputting a detection result of the pine wood nematode disease. The method effectively solves the problems of overexposure, underexposure or noise amplification easily occurring under the condition of weak light in a traditional method, improves the detection precision of the model in a low-light environment, and achieves the purpose of meeting the real-time processing requirement of mobile terminal equipment while maintaining the feature extraction capability.

The application discloses a pine wood nematode disease tree detection method based on unmanned aerial vehicle vision, which comprises the following steps: acquiring pine forest images and geographic information collected by unmanned aerial vehicle aerial photography; obtaining a fused pine forest image by fusion; dividing the fused pine forest image into a plurality of image blocks with the same size; obtaining an image block set after data cleaning; labeling each image block to obtain a training set; constructing a target detection model based on a Color-ViT-YOLOv8 algorithm; replacing the backbone network in YOLOv8 with a ViT module and adding a Color module before the ViT module; processing the input image by using a channel selection and fusion technology of a multi-dimensional color space in the Color module; training the target detection model by using the training set to obtain a trained target detection model, which is used for pine wood nematode disease tree detection on a to-be-detected pine forest image. The application can solve the false detection and missed detection problems in the pine wood nematode disease tree detection in a complex scene.

The invention provides a Bletilla striata planting method under a Pinus elliottii forest, relates to the technical field of under-forest economy, and solves the problems of long growth cycle and slow economic benefit of a traditional Pinus elliottii planting forest, and the method comprises the following steps: (1) planting Pinus elliottii seedlings according to specific specifications; (2) after the wetted pines grow for 3 years, ridging between the line spacing of the wetted pines to plant rhizoma bletillae seedlings; (3) covering ridge surfaces with pinus armandii shells; (4) performing high fertilizer and water management; according to the method, by means of the semi-shade environment formed by the canopy of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines of the pines.

The utility model discloses a pine cone picking device for an unmanned aerial vehicle, relates to the technical field of pine cone picking, and aims to solve the problems that an existing mechanical pine cone picking device can cause damage to pine trees, and large mechanical equipment cannot enter a pine forest to work, so that the mechanical applicability is low. Comprising a hexagonal bracket, picking claws, a traction device frame and a traction device, a picking claw is hinged to each side wall of the hexagonal support, a plurality of picking teeth are arranged at the lower end of each picking claw, a vertical traction part perpendicular to the picking claws is arranged at the hinged position of the picking claws, a traction device frame is arranged at the upper end of the hexagonal support, and a traction device used for driving the picking claws to be opened or closed is arranged in the traction device frame. The traction device comprises an upper guide roller, a lower guide roller, a motor, an upper traction rope, an upper picking claw traction rope, a lower traction rope and a lower picking claw traction rope. The pine cone picking machine is used for picking pine cones.

The invention discloses a method for mixed planting of roughhaired holly, sweetgum and poria cocos. The method is a new under-forest economic compound management afforestation mode with mixed arbor and shrub, intercropping of forest, fungi and drugs and combination of short, medium and long. Pine forest cutting slash is selected, holly root and sweetgum are planted in an arbor-shrub mixed mode according to the proportion of 1: 1, the density is 260-400 plants per mu, early vegetation coverage of the afforestation land can be accelerated, the amount of fallen leaves on the forest land can be increased, soil fertility can be improved, and forest growth can be promoted; 130-200 plants of roughhaired holly are intercropped and intercropped per mu and can be harvested as a traditional Chinese medicinal material after 5 years, and the economic income per mu is increased by 2000-3000 yuan; poria cocos fungi are planted in pine stumps, the production cycle is 1-2 years, the economic income can be increased by about 2000 yuan per mu, and the Poria cocos fungi accelerate rotting and decomposing of the stumps, increase the content of soil organic matter, improve the soil structure and promote growth of roughhaired holly and sweetgum.

The invention relates to the field of pest prevention and control, in particular to a method for preventing and controlling monochamus alternatus by utilizing eyespot beetle knocking eggs, which comprises the following steps: 1) collecting eyespot beetle knocking imagoes in a pine forest; (2) continuously feeding the collected eyeshield adult beetles in the breeding box; (3) collecting eggs laid by the adult eyeshield beetles; and (4) releasing the collected ocultinia oculi eggs to a pine forest area in which monochamus alternatus pests are to be prevented and controlled. According to the method, the population density of the monochamus alternatus in the pine forest region is continuously controlled to be at a low level by utilizing the food chain advantage of the percussion oculi, and compared with a chemical mode for preventing and controlling the monochamus alternatus in the prior art, the technical scheme provided by the invention has the great advantages of environmental protection, strong prevention and control effect persistence and low technical cost, and is suitable for popularization and application in the field of prevention and control of the monochamus alternatus in the pine forest region. The method is suitable for large-scale popularization and application.

The application discloses a kind of mobile monitoring platform based on thermal imaging camera, including mobile robot mechanical chassis, host computer, multi-line laser radar and thermal imaging camera.It is suitable for mobile robot field.It solves the technical problems that it is difficult to apply monitoring platform on mobile robot, secondly, it is difficult to plan path and navigate obstacle avoidance for robot, then, it is difficult to complete target object identification and temperature extraction, finally, it is difficult to complete target object identification work using thermal imaging camera in industrial scene.The application adopts modular structure, which is convenient for disassembly and maintenance in later period, and is easy to install and use.The application adopts pine forest four-wheel chassis, which has high mechanical strength and is convenient for crossing speed bump and other road obstacles.The application adopts Leshen multi-line laser radar, which can simulate real three-dimensional environment.The application measures ambient light and temperature field using Hikvision thermal imaging camera, and can complete temperature extraction of target object using multi-modal detection method.

The present application relates to the technical field of green control of Dendroctonus valens, and particularly relates to a Dendroctonus valens repellent, a preparation method and application thereof. The repellent component comprises opoponax oil. It is found by the present application that opoponax oil has obvious repellent effect on Dendroctonus valens, and opoponax oil can be used as a Dendroctonus valens repellent, and the Dendroctonus valens can be repelled in production to protect the pine forest ecosystem. The repellent has good repellent effect, is stable, is environment-friendly, and has broad industrialization prospects.

The invention discloses a multi-stage pine wood nematode disease detection method, and belongs to the technical field of pine wood nematode disease color-changing epidemic wood detection.The method comprises the steps that multispectral image samples are labeled according to different pine wood disease infection stages, a multispectral image sample training set with labels is adopted to train a pine wood nematode disease detection model, and the pine wood nematode disease color-changing epidemic wood color-changing epidemic wood color-changing epidemic wood color-changing epidemic wood color-changing epidemic wood is obtained; the trained pine wood nematode disease detection model is utilized to identify the pine wood nematode disease change stages of the target pine forest multispectral image, so that the epidemic woods in different pine wood nematode disease change stages can be accurately identified, and the problem that the current pine wood nematode disease detection method is low in identification precision in different pine wood nematode disease change stages is solved.

1. The name of the present design product: dress (amber pine forest). 2. The use of the present design product: for daily wearing. 3. The design points of the present design product: in the combination of shape, pattern and color. 4. The picture or photo that can best show the design points: front view. 5. The design claimed contains color. 6. The present design product is a thin product, and other views are omitted.

The invention discloses a soil improvement matrix under Yunnan pine forest as well as a preparation method and application thereof, and belongs to the technical field of under-forest soil remediation. The substrate is prepared from the following raw materials in percentage by weight: 55%-65% of fermented and crushed traditional Chinese medicine residues, 10%-20% of fermented and crushed pine needles and pine cones, 10%-20% of perlite, 3%-7% of potassium fulvate and 3%-7% of bacillus subtilis powder. The preparation method comprises the steps of raw material dry mixing, active ingredient adding, moisture adjusting, stacking activation and the like. The matrix can effectively improve the physicochemical properties of acidic hardened soil under the Yunnan pine forest, significantly improve the content of organic carbon, total nitrogen, total phosphorus and total potassium in the soil and the available nutrient supply capacity, adjust the pH value of the soil, enhance the fertility of the soil and promote the growth of the forest, and has the advantages of low cost, environmental protection and easiness in popularization.

The invention discloses a forest pine wood nematode disease intelligent detection method and system, and belongs to the technical field of image recognition and target detection, and the method comprises the steps: obtaining forest pine wood historical images, and constructing a pine wood image sample training set; and constructing and training a pine wood image recognition network based on the pine wood image sample. A pine forest is divided into a plurality of areas, and pine forest images of different areas are acquired. And performing preliminary identification on the acquired pine forest image based on the first identification network, and outputting a suspected disease image. And performing deep identification on the suspected disease image based on a second identification network, and outputting a disease detection result. According to the method, characteristics such as growth potential are obtained through the CNN network, an accurate threshold value is set for preliminary screening, the preliminarily screened area is further judged through the improved MobileNet V3-small network, basic characteristics such as needle leaf color change, growth potential stop and resin secretion of initial symptoms of the pine wood nematode disease are fully considered, and the accuracy of the initial symptoms of the pine wood nematode disease is improved. And the detection capability and the recognition precision of sparse small disease spots in the early stage of pine wood nematode disease are enhanced.

The pine wilt diseased tree detection method based on small target sample expansion and pooling weighting specifically includes the following steps: Step 1: Photograph pine forests under different lighting and terrain environments to obtain original images; Step 2: Crop the original images, mark the cropped images with pine wilt diseased trees, and create a pine wilt diseased tree dataset; Step 3: Use a small target sample expansion method with fixed scaling and random splicing to increase the number of diseased trees in the image, especially the number of small-scale diseased tree targets; Step 4: Construct a target detection network based on pooling weighted attention mechanism feature enhancement; Step 5: Input the pine wilt diseased tree dataset into the pooling weighted attention mechanism feature enhancement target detection model for training to obtain a diseased tree recognition model; Step 6: Input the pine forest image to be identified into the recognition model for recognition to obtain the vector position information of the diseased trees; Step 7: Clean up the diseased trees based on the diseased tree position information.

The invention relates to the technical field of computer graphics and three-dimensional modeling, and discloses a pine forest three-dimensional model rapid reconstruction method based on unmanned aerial vehicle photography, which comprises the following steps: acquiring a multi-view image sequence, extracting a single pine topology skeleton line, and calling a parameterized fractal generation program to construct a virtual three-dimensional geometry; performing Euclidean distance transformation on the contour of the image entity to generate a distance field graph, and calculating the projection consistency deviation of the virtual geometry in the distance field graph; and according to the deviation gradient, iteratively correcting the geometric growth control parameters until convergence, and finally instantiating and loading a preset leaf cluster model at the tail end of the skeleton to generate a three-dimensional grid. The problems of reconstruction damage and non-convergence caused by rigid matching failure of a discrete discontinuous object in a dynamic environment are solved, reverse recovery of a high-fidelity topological structure under low data density is realized, and the topological structure closure and geometric attitude convergence stability of a reconstruction model are ensured.

The invention discloses a method for preventing and controlling bark beetles by utilizing Scleroderma guani to carry microbial bacteria, which comprises the following steps: activating and propagating bacillus MJ-4 to obtain bacillus MJ-4 bacterial liquid, directly or indirectly applying the bacillus MJ-4 bacterial liquid to the body of the Scleroderma guani, and then releasing the Scleroderma guani carrying the bacillus MJ-4 bacterial liquid to trees of a pine forest land. When the scleroderma guani find food, the scleroderma guani can automatically find and contact the bark beetles, and the bacillus MJ-4 in the bacterium liquid carried by the scleroderma guani can invade and kill the bark beetles. The biological prevention and control mode that the bark beetles carry bacteria is adopted, according to the characteristic that the bark beetles are potentially damaged in pine trees, the natural enemy scleroderma guani is used as a medium to carry the biocontrol microbial inoculum to conduct biological prevention and control on the bark beetles, the bark beetle killing effect is remarkable, and the mode is free of toxic effects on higher animals and plants, free of environment pollution and suitable for popularization and application. The method has a sustainable control effect, solves the problems of high prevention and control difficulty, poor prevention and control effect and the like of the bark beetles, and provides a new way for biological prevention and control of the bark beetles in the forest land.

The present invention provides a method and device for early detection of pine nematode disease, which belongs to the field of image processing technology and includes the following steps: obtaining monitoring images of a target pine forest at multiple observation times; extracting feature points from each monitoring image to obtain homonymous points for each monitoring image; performing coordinate correction on all monitoring images based on the homonymous points to determine reference information for each pine tree and patch parameters for each patch; generating evaluation parameters for patches on each pine tree based on the reference information for each pine tree and the patch parameters for each patch; and determining the early stage of nematode disease in the target pine forest based on the evaluation parameters. The present invention provides a method and device for early detection of pine nematode disease, based on high-resolution visible light data of infected trees that changes over time, by tracking the temporal process of patch occurrence and development in the canopy of the pine forest, achieving high-precision early diagnosis of pine wilt disease with a low false alarm rate and high universality, effectively reducing ecological and economic losses.