526 results about "Crop yield" patented technology

The invention provides a trichoderma maple fungicide, a microbial granular fertilizer as well as a preparation method and application of the trichoderma maple fungicide and the microbial granular fertilizer, and relates to the field of microorganisms. The acer rubrum fungicide contains acer rubrum RA with the preservation number of CGMCC No.35889. The acer rubrum fungicide has the capability of decomposing inorganic phosphorus, is wide in phosphorus dissolving range, can decompose tricalcium phosphate, ferric phosphate and aluminum phosphate, and particularly still has the very strong capability of decomposing the inorganic phosphorus under the low-temperature condition; the strain has the capability of producing siderophore and IAA at low temperature; the strain has an inhibition effect on various pathogenic bacteria causing field crop diseases. The microbial granular fertilizer provided by the invention contains various biostimulant substances, can promote crop growth, improve plant stress resistance and improve plant resistance to plant diseases and insect pests, and field tests prove that the microbial granular fertilizer can significantly promote crop growth and improve crop yield.

The invention provides a moniliella malayi and a fungicide and application thereof, and relates to the field of microorganisms. The acer rubrum is specifically acer rubrum RA, and the preservation number of the acer rubrum RA is CGMCC (China General Microbiological Culture Collection Center) No.35889. The acer rubrum RA can be used for preparing the acer rubrum. The strain has the capability of decomposing inorganic phosphorus, is wide in phosphorus-dissolving range, can decompose tricalcium phosphate, iron phosphate and aluminum phosphate, and particularly still has very strong capability of decomposing inorganic phosphorus under a low-temperature condition; the strain has the capability of producing siderophore and IAA at low temperature; the strain has an inhibition effect on various pathogenic bacteria causing field crop diseases, and can inhibit diseases such as corn root rot, sorghum leaf spot or wheat root rot; the strain can be colonized in a soil environment of 5-40 DEG C, a microbial inoculum prepared from the strain can promote crop growth, the crop yield is remarkably improved, and high yield can be achieved even if the usage amount of phosphate fertilizer is reduced.

The invention belongs to the technical field of biological mineral fertilizers, and particularly relates to a microbial liquid complex microbial inoculant with coal gangue activation and heavy metal treatment functions and a biological mineral fertilizer. The microbial liquid complex microbial inoculant is prepared from the following functional strains by using electrochemical low-Hertz water through sequential inoculation and fermentation in three stages: repair bacteria pseudomonas putida, nitrogen-fixing bacteria azotobacter chroococcum, growth-promoting bacteria azotobacter braziliensis, phosphate solubilizing bacteria pseudomonas fluorescens, biocontrol bacteria bacillus subtilis and potassium bacteria bacillus mucilaginosus. The silicolytic bacteria are bacillus megatherium; and the synergistic bacteria are trichoderma asperellum. The biological mineral fertilizer is prepared by using the microbial liquid complex microbial inoculant and using coal gangue powder as a main raw material. The scientific compatibility of different strains provided by the invention realizes the efficient improvement of the heavy metal polluted land and the yield increase of crops, changes the coal gangue into valuables, solves the problem of pollution caused by the accumulation of a large amount of coal gangue, and has important economic and social values.

Protecting open seed trenches from ingress of foreign material, such as plant residue. Preventing foreign material from entering the open seed trench during planting increases crop yield by minimizing delayed germination and late plant emergence. Various implementations of adjustable row cleaners may be used to prevent ingress of foreign material. Also, various shields may be disposed on the row units of a planter to prevent ingress of foreign material.

The invention relates to the technical field of agricultural planting management, in particular to a crop precise planting decision system based on multi-modal data fusion and artificial intelligence, which comprises a multi-modal data acquisition module, a data fusion and processing module, an artificial intelligence decision engine, a strategy execution module and a self-learning optimization module, the multi-modal data acquisition module acquires environmental crop farming data; the data fusion and processing module generates a fusion data set with unified space-time labels and establishes a plant electronic file; the artificial intelligence decision engine simulates growth through a crop growth digital twinborn model, intelligently diagnoses deviation and generates a precise decision; the strategy execution module drives the automation equipment to execute a decision; the self-learning optimization module is based on a feedback iterative optimization model and an agronomic rule base. The method improves the decision scientificity and precision, reduces the resource waste, guarantees the crop yield and quality, is suitable for different planting scenes, and meets the precise and large-scale planting demands.

The invention provides a crop yield estimation method and device, electronic equipment and a storage medium, and relates to the technical field of agricultural information technology, remote sensing monitoring and artificial intelligence crossing, and the method comprises the steps: carrying out VOD space-time fusion processing based on multi-source microwave remote sensing data through a VOD space-time fusion model, and obtaining VOD fusion data; simulating crop growth based on the environment data and the crop planting data through a target crop growth model to obtain crop physiological process data; constructing multi-source feature data based on the VOD fusion data, the crop physiological process data and the environmental data; and through a double-flow space-time deep learning model, carrying out space-time feature extraction and fusion on the multi-source feature data to obtain a biomass estimated value, and further calculating a crop yield estimated value. According to the method, the crop yield is estimated based on fusion of the multi-source microwave remote sensing data, the crop growth model and the deep learning model, the restriction of a single technical normal form is broken through, and the accuracy of an estimation result can be improved.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmostpheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

This disclosure generally relates to removing or reducing a time delay between sensor data using a computational, inexpensive numerical method or a machine learning model to generate more accurate prescription maps used to perform future work operations. In one aspect, a first time-stamped signal and a second time-stamped signal are cross-correlated to apply a time delay to the first time-stamped signal to generate a time-invariant data set. The first time-stamped signal corresponds to sensor data indicative of mass flow rate, and the second time-stamped signal corresponds to sensor data indicative of engine torque. In one aspect, a machine learning model is trained to remove the time delay of the first time-stamped signal based on labeled time delays corresponding to different sensor signals. Reducing effects of these time delays associated with sensor data can improve overall farming operations, enhance crop output, increase operational efficiency of work vehicles, and reduce fuel emissions.

A fusion network model-based crop yield inversion method and system, the method includes: obtaining meteorological data and remote-sensing image data in a study region; building a Long Short-Term Memory-Convolutional Neural Networks (LSTM-CNN) coupling neural network model by using LSTM and CNN; performing pretraining on the LSTM by using simulation data generated by a WOFOST crop growth model, freezing LSTM layer parameters, and training the LSTM-CNN coupling neural network model based on the meteorological data and the remote-sensing image data to obtain a crop yield inversion model. In the present disclosure, by combining the advantages of the crop growth model and the neural network model, information extraction is performed on meteorological data and remote-sensing image data respectively, and feature information is fused into one feature vector. Thus, prior yield inversion information can be obtained from the meteorological data and the land plot information can be introduced.

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

The present disclosure provides agronomically stable liquid agricultural compositions, methods of formulation thereof, and methods of application thereof. The agricultural compositions comprise nitrogen-fixing microorganisms and one or more of a buffering agent, a microbial stabilizer, and a physical stabilizer. The disclosed liquid agricultural compositions have a longer shelf life and greater ease of application than other existing dry and liquid formulations. The disclosed liquid agricultural compositions are stable for a period of thirty days or longer with low toxin accumulation and high microbial stability. The compositions are suitable for use on agricultural plant tissues or the environs thereof for providing a source of fixed atmospheric nitrogen to the agricultural plant. The compositions are used to increase crop yield and decrease yield variance.

The invention provides an automatic irrigation device and method based on a soil humidity sensor, and relates to the technical field of water-saving sprinkling irrigation. The automatic irrigation device comprises a distributed soil humidity sensor network buried in soil of an area to be irrigated, a central controller connected with the distributed soil humidity sensor network and an irrigation execution unit electrically connected with the central controller. By arranging a distributed soil humidity sensor network and constructing a three-dimensional distribution model of soil moisture, the inherent defect that traditional single-point measurement is over-summarized is thoroughly overcome; by means of the automatic irrigation device, the real moisture conditions of each subarea and different depths in the irrigation area can be accurately mastered, so that independent and on-demand irrigation of each subarea is achieved, the high uniformity of the soil humidity of the whole field is effectively guaranteed, the optimal moisture environment is provided for crop growth, and the crop yield and quality are directly improved.

The invention discloses a three-dimensional point cloud modeling method and system based on a multi-view image, and relates to the technical field of agricultural intelligent perception, and the method specifically comprises the following steps: collecting a multi-view image in a farmland environment based on a depth camera, analyzing the overlapping region and boundary characteristics of point cloud data, recognizing a plant, and segmenting the phenotypic structure of the plant; and adjusting geometric correction parameters, performing growth dynamic grading and yield prediction, and obtaining a crop yield potential distribution data set in combination with crop phenotype information and growth dynamic grading. According to the invention, the depth camera is used for collecting the multi-view image, analyzing the spatial distribution characteristics of the crop target and the overlapping region and boundary characteristics of the point cloud data, optimizing geometric correction parameters, improving the point cloud alignment precision, ensuring the accuracy in the splicing process, improving the quality of the point cloud splicing result, and improving the quality of the point cloud splicing result in a complex farmland environment. Accurate extraction and phenotypic structure segmentation of crop targets are ensured, crop growth dynamics are accurately evaluated, and the intelligent level of agricultural production management is improved.

The invention relates to a male gamete killer gene S19g-A1 of a rice interspecific hybrid sterile S19 seat and application of the male gamete killer gene S19g-A1. The invention provides a rice hybrid sterility related protein S19g-A1. The amino acid sequence of the rice hybrid sterility related protein S19g-A1 is as shown in SEQ ID NO. 2. According to the invention, a key gene S19g-A1 of a rice interspecific hybrid sterile S19 site is researched and identified, function knockout is carried out on African rice S19g-A1, and pollen fertility of a mutant hybrid F1 obtained by carrying out distant hybridization on an obtained African rice s19g-a1 mutant returns to normal. Based on a gene editing technology, the invention develops a method for rapidly creating a hybrid affinity line capable of overcoming sterility of African rice hybrid, effectively eliminates S19 locus mediated interspecific hybrid reproductive disorder, breaks through the bottleneck of rice interspecific hybrid advantage utilization, provides target gene resources for distant hybrid advantage utilization and crop yield improvement, and has a wide application prospect. Good application prospects are realized.

The invention provides application of Ustilaginoidea virens secretory protein UvWMT and a coding gene thereof in prevention and control of plant diseases. The gene sequence of the Ustilaginoidea virens secretory protein UvWMT is researched, found and cloned for the first time, and the new application of the Ustilaginoidea virens secretory protein UvWMT in disease-resistant breeding of crops such as rice is developed. Research finds that the protein is a key pathogenic factor in the process that rice is infected by ustilaginoidea virens, heterologous expression experiments further find that heterologous expression of UvWMT in rice can significantly improve the resistance of crops to various pathogenic bacteria such as ustilaginoidea virens, magnaporthe oryzae and xanthomonas oryzae, and has no significant difference in key agronomic traits compared with a wild type. An important theoretical basis is provided for rice disease-resistant breeding, crops can be safely and effectively endowed with broad-spectrum disease resistance by heterologous expression of the gene, and the gene has great application prospects and economic values in the aspects of crop breeding disease resistance improvement, rice disease prevention and control and crop yield increase and pesticide reduction.

The invention discloses a multi-source data driven irrigation district resource regulation and control method and system fused with a neural network, and relates to the technical field of irrigation district resource regulation and control, and the method comprises the steps: collecting irrigation district multi-source data, carrying out the cleaning, standardization and time-space dimension reconstruction processing, constructing a hybrid neural network regulation and control model fused with CNN spatial feature extraction and LSTM time sequence modeling, and carrying out the calculation of the hybrid neural network regulation and control model. A weighted mean square error is used as a loss function, model training is completed in combination with an Adam optimizer and hyper-parameter optimization, and finally optimal regulation and control parameters meeting resource constraints and crop requirements are output; according to the method, the defects that traditional regulation depends on artificial experience and the multi-source data fusion capability is weak are effectively overcome, data space-time correlation information is fully mined, scientificity and accuracy of irrigation area resource regulation are improved, water resource waste and fertilizer loss are reduced, accurate matching of crop supply and demand is achieved, the crop yield is guaranteed, the ecological environment is improved, and the economic benefit is increased. The method is suitable for multi-resource cooperative regulation and control of complex irrigation areas.

The invention belongs to the technical field of smart agriculture and Internet of Things, discloses a smart agriculture-oriented heterogeneous Internet of Things data fusion and multi-model collaborative optimization method, and aims to solve the problems of high data isomerism, low fusion precision, single regulation and control strategy and insufficient adaptive ability of the traditional agricultural Internet of Things. The method comprises the steps of collecting multi-dimensional agricultural data through a distributed heterogeneous Internet of Things terminal, and generating a standardized feature data set through multi-level data fusion processing; constructing a three-stage model collaborative architecture of'spatio-temporal feature extraction-growth demand prediction-multi-objective optimization ', and dynamically adjusting model parameters in combination with a crop growth cycle; and realizing model adaptive iteration based on reinforcement learning, and outputting an optimization regulation strategy considering the crop yield, the resource utilization rate and the low-carbon target. Through heterogeneous data deep fusion and multi-model collaborative decision making, accurate matching of the agricultural environment and crop growth is realized, the method is suitable for various intelligent agricultural scenes such as greenhouses and field planting, and the intelligence and sustainability of agricultural production are remarkably improved.

The invention relates to the technical field of crop yield estimation through remote sensing images, and provides a flue-cured tobacco yield prediction method fusing airborne laser radar point cloud and an orthoimage, and the method comprises the steps: obtaining the LiDAR point cloud and the orthoimage of a measurement region; performing tobacco field identification, tobacco field two-dimensional polygon extraction and tobacco field area calculation on the orthoimage by using a pre-trained U-Net semantic segmentation network model; tobacco field leaf area index extraction is carried out based on LiDAR point cloud, and then a tobacco field average leaf area index is calculated; obtaining a tobacco leaf surface density value of the tobacco field; and predicting the flue-cured tobacco yield based on the tobacco field area, the tobacco field average leaf area index and the tobacco leaf surface density value of the tobacco field. A new method is provided for flue-cured tobacco yield prediction, the method has the advantages of remarkably reducing the number of field workers, the workload and the labor intensity, and the method is free of contact and damage, simple, easy to implement, high in precision, high in timeliness and economical and feasible.

Suppressing methane gas emissions from paddy rice cultivation is extremely important as a measure against global warming. The challenge here is to achieve both improved rice productivity and environmental protection. Specifically, this involves developing a device that can generate large quantities of high-concentration ultrafine bubble (UFB) water in a short time, by adding methane gas emission suppression to the UFB water. [Solution] The UFB generating apparatus of the present invention incorporates a variable-speed rotary stirring device into a UFB generation tank, and this tank is filled with sandy material. By supplying gas and water to this tank at high pressure, the sandy material layer of gas, water, and sandy material is liquefied. By rapidly rotating this liquefied layer with the rotary stirring device, the gas is confined in the pore water of the liquefied layer and then finely fragmented. Furthermore, by using sandy steel slag as the sandy material, in addition to the UFB effect, methane generation due to silica, lime, and iron oxide is suppressed and crop yields are improved.

The invention relates to the technical field of agricultural production resource big data processing, and discloses an agricultural production resource configuration optimization and management system based on deep learning, which realizes deep fusion of macroscopic remote sensing and microscopic sensing data by constructing a space-time heterogeneous graph neural network, effectively solves the problem of mismatching of space-time scales of multi-source agricultural data, and improves the accuracy of agricultural production resource configuration optimization and management. By using the domain adversarial training and meta-learning technology, the system can extract environment-independent general crop characteristics from source domain historical data, and by combining physical mechanism constraints, the rapid self-adaption of the model can be completed in a target new farm only by using a very small number of samples, so that the data acquisition cost and the model deployment period are remarkably reduced, and the model deployment efficiency is improved. In addition, a physically guided loss function ensures that a prediction result accords with a biological law, and accurate on-demand distribution of water and fertilizer resources is realized in combination with a reverse resource mapping algorithm, so that agricultural production resources are greatly saved while the crop yield is increased, and the robustness and generalization ability of an intelligent system in a complex and changeable environment are enhanced.

An object of a system according to an embodiment is to improve the efficiency of agricultural management by analyzing agricultural data and measuring the state and moisture content of soil in real time.SOLUTION: A system includes a data analysis unit, a sensor measurement unit, a prediction unit, and a consulting unit. The data analysis part analyzes the agricultural data. The sensor measurement unit measures the state and moisture content of the soil in real time. The prediction unit predicts future crop production based on past data. The consulting unit provides an optimal solution according to the situation of the farm.SELECTED DRAWING: Figure 1

The invention provides an application of a Ustilaginoidea virens UvWSC1 gene in improvement of plant disease resistance. The invention develops a new application of the Ustilaginoidea virens UvWSC1 gene in improving the broad-spectrum disease resistance of crops such as rice for the first time, and the UvWSC1 gene is subjected to heterologous high expression in the rice, so that the broad-spectrum resistance of the rice to various pathogenic bacteria such as Ustilaginoidea virens, Magnaporthe oryzae and Xanthomonas oryzae can be obviously enhanced, and the agronomic traits of the rice are not influenced; an important theoretical basis is provided for rice disease-resistant breeding. By heterologous expression of the UvWSC1 gene, broad-spectrum disease resistance can be safely and effectively endowed to crops, a broad-spectrum disease-resistant rice variety is obtained for production, and the broad-spectrum disease-resistant rice gene has great application prospects and economic values in the aspects of crop breeding disease resistance improvement, rice disease prevention and control and crop yield increase and pesticide reduction.

The invention belongs to the technical field of soil amendments, and particularly relates to a saline-alkali soil biological-mineral composite amendment prepared from industrial waste and a preparation method thereof. Alkali fusion activated steel slag provides a quick-acting active calcium source, hydrothermal modification neutralized red mud forms hydrated aluminosilicate gel solid sodium, modified biochar enhances cation exchange capacity, a composite functional bacterial agent drives MICP biomineralization reaction, a quick-acting-slow-release-storage three-stage gradient improvement system is established, and the quick-acting-slow-release-storage three-stage gradient improvement effect is achieved. According to the present invention, the effects of 0-50 cm soil profile full-layer improvement and 12-24 month continuous improvement are achieved, 90 d after the modifier is applied, the soil pH value of the severe saline-alkali soil is reduced to 7.8 from 10.1, the conductivity is reduced to 3.2 dS / m from 11.5 dS / m, the sodium adsorption ratio is reduced to 11 from 42, the crop yield increase amplitude reaches 40-60%, 15-25 kg of the modifier is fixed per ton, and the industrial waste resource utilization rate gt is achieved; 90%.

The invention discloses streptomyces pseudogriseogriseus for promoting straw degradation and increasing soil organic matters, and belongs to the technical field of microorganisms. The streptomyces pseudogriseolus NF3 disclosed by the invention has the activity of CMC-Na enzyme, xylanase, laccase, lignin peroxidase and manganese peroxidase, and can be used for degrading cellulose, hemicellulose and lignin at the same time, and the degradation rate of the streptomyces pseudogriseolus NF3 on corn straws reaches 67.75%; the fertilizer is applied to straw returning soil, soil nutrient release is accelerated, the soil organic matter content is increased, and the crop yield is increased.

The invention discloses a main crop accurate water and fertilizer preparation method for reducing drainage pollution in a Yellow River irrigation area, and relates to the technical field of crop cultivation, and the method comprises the following steps: S1, collecting multi-source data of a sunflower planting field in the Yellow River irrigation area; s2, constructing a regional water and fertilizer response model, and performing calibration and verification; s3, constructing a response relation library by using the response function obtained in the step S2, and inputting the response relation library as a multi-objective optimization model; s4, establishing a multi-objective optimization model based on the response relation library; and S5, solving the optimal water and fertilizer configuration scheme by adopting an improved non-dominated sorting genetic algorithm, and outputting the irrigation amount and the nitrogen application amount decomposed according to the growth period. The regional water and fertilizer response model is used for simulating the crop yield, the water utilization efficiency and the nitrogen utilization efficiency under different irrigation amount and fertilization amount conditions, the response relation of crops to water and fertilizer can be quantified, and the water and fertilizer utilization rate is comprehensively considered in the multi-objective optimization process.

The invention relates to the field of microbial agents, in particular to a micro-ecological restoration liquid composite microbial agent suitable for heavy metal contaminated soil and a biological bacterial fertilizer and a preparation method thereof. The micro-ecological restoration liquid complex microbial inoculant suitable for the heavy metal contaminated soil comprises nitrogen-fixing bacteria: azotobacter chroococcum ACCC 11104, phosphate solubilizing bacteria: pseudomonas fluorescens ACCC 03031, potassium solubilizing bacteria: bacillus mucilaginosus CCTCC KB20082790, growth-promoting bacteria: azotobacter braziliensis CCTCC AB 91001, bacillus subtilis CGMCC 17161, synergistic bacteria: trichoderma asperellum CGMCC 3.17461, and restoration bacteria: pseudomonas putida CGMCC 1.4533. The biological bacterial fertilizer for the heavy metal contaminated soil is prepared from the liquid complex microbial inoculant, compared with a conventional biological bacterial fertilizer, the adaptability to the heavy metal contaminated soil is greatly improved, the heavy metal contaminated soil is effectively improved, and the crop yield is remarkably increased.

Plant analysis system includes a vehicle configured to traverse a field in which the plant is growing and an imaging device mechanically coupled to the vehicle. Imaging device is configured to generate stereo image data associated with the plant. A back-end computer system configured to store a machine learning algorithm that, when executed by a processor, cause the back-end computer system to receive the stereo image data from the imaging device, autonomously detect an object of interest associated with the plant based on the received stereo image data, characterize the detected object of interest, and estimate a crop yield based on the characterization of the detected object of interest.

The invention relates to the technical field of agricultural engineering, in particular to a self-adaptive greenhouse environment control system which comprises a sensing layer, a decision-making layer, an execution layer and an optimization layer, and the sensing layer, the decision-making layer, the execution layer and the optimization layer achieve inter-layer data penetrating interaction and self-adaptive resource scheduling through a cross-layer dynamic cooperation mechanism. The sensing layer is used for collecting greenhouse environment parameters, crop physiological parameters and greenhouse physical structure parameters; the decision-making layer is used for carrying out collaborative decision-making through a physical model module and a self-adaptive MPC algorithm module based on the data collected by the sensing layer, and generating a control instruction; the execution layer is used for operating greenhouse environment regulation and control equipment according to the control instruction of the decision layer; and the optimization layer is used for dynamically adjusting the optimal combination of the equipment based on a preset regulation and control target. Through coupling of the cross-layer collaborative architecture, the physical model and the self-adaptive MPC algorithm, the greenhouse environment regulation and control precision is improved, the energy consumption is reduced, the per unit yield of crops is increased, and the labor and equipment cost is reduced.

The present application relates to a kind of agricultural machinery granary and its material level on-line detection method, including whole vehicle information terminal, signal processor, light source, bar sensor.Whole vehicle information terminal is used to display the final result of granary material level, it is a man-machine interactive interface;Signal processor is whether the data of control light source works, reads bar sensor, integrates data and completes the calculation of granary material level data;Light source works as the input source of bar sensor;Bar sensor belongs to optical sensor, when sensor is covered by grain, the input of light source is shielded.A kind of granary material level detection method refers to, through signal processor, light source is lit, and the photosensitive information of bar sensor is read in real time, after summarizing calculation, the final result of granary material level is obtained, and is uploaded to whole vehicle information terminal.The present application can realize the real-time on-line monitoring of crop yield.

Disclosed herein are apyrase inhibitors of Formula (I). Also disclosed herein are methods for using the disclosed inhibitors, including in methods for protecting crops from pests. In one aspect the apyrase inhibitors are useful for enhancing the activity of pesticides for the protection of crops from pathogens and to support crop yield.