305 results about "Agricultural information" patented technology

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The invention discloses an intelligent wheat yield prediction method and system, and relates to the technical field of agricultural information. According to the method, basic geography, climate, soil, crop physiology and agricultural management multi-source data are collected, and an input feature set is obtained through preprocessing and feature engineering; constructing an Attention-LSTM-CNN model fused with an improved attention mechanism, extracting local features through CNN, capturing time sequence association through LSTM, highlighting key contribution through an attention layer, and finishing model training in combination with an RMSE loss function and an Adam optimizer; and dynamically updating data and a prediction result in a wheat growth cycle, and outputting and triggering early warning in multiple forms. The system correspondingly comprises a data acquisition module, a data storage module, a data preprocessing module, a model calculation module, a prediction output and early warning module and a communication module. The method solves the problems that a traditional method is single in data, poor in model adaptability and lack of dynamic prediction, high-precision full-period prediction is achieved, and scientific support is provided for agricultural decision making.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

The invention provides a crop growth prediction method and system based on multi-modal data, and relates to the technical field of agricultural information, and the method comprises the steps: 1, collecting real-time environment data and crop physiological data of a heterogeneous multi-source sensor in a greenhouse environment, and constructing a multi-modal original data set; 2, performing preprocessing and space-time alignment on the multi-modal original data set to form a multi-modal data fusion matrix with a unified timestamp; step 3, based on the multi-modal data fusion matrix, selecting a reference data feature set, generating an environmental evolution mode and a physiological evolution mode, and calculating a correlation degree between the two modes to determine a feature response dimension; and respectively selecting dynamic monitoring feature sets inside and outside the feature response dimension, constructing a feature evolution trajectory according to a time sequence evolution relationship, and generating a dynamic compensation coefficient. By integrating environment and crop physiology multi-source data, the association rule of crop growth and environment is predicted, and the agricultural production efficiency is improved.

The invention relates to the technical field of agricultural informatization and supply chain risk control, and particularly discloses an agricultural supply chain risk intelligent sensing and early warning system based on multi-source data fusion. According to the system, six multi-source data including a producing area environment, an agricultural product category, a transaction behavior, a logistics link, credit and market information are integrated through a data acquisition layer; space-time alignment and cleaning are carried out through the data processing and alignment layer; through feature engineering and a multi-source fusion layer, depth features are constructed by comprehensively utilizing a knowledge graph and a graph attention network, Transform behavior sequence modeling, CNN-LSTM remote sensing time sequence analysis and Graph2Seq logistics trajectory prediction, and the Transform and the graph attention network are specially adapted for an agricultural scene; the dynamic risk scoring layer is used for comprehensively calculating five risks of producing areas, logistics, transactions, markets and credit, and a comprehensive risk score is output through weighted fusion; a decision basis is provided by integrating SHAP, attention visualization and map path tracking through an interpretability output layer; and finally, outputting a grading risk strategy by a strategy decision-making layer. According to the invention, dynamic, accurate and explainable intelligent assessment and early warning of full-link and multi-dimensional risks of the agricultural supply chain are realized.

The invention provides an insect pest prediction method based on PSO-LSTM, belongs to the technical field of agricultural information, and aims to solve the problems that a single machine learning model is adopted in a traditional insect pest prediction method, the long-term modeling capability of time sequence data is insufficient, the complex nonlinear relation between the number of insect pests and meteorological factors is difficult to capture, and the prediction precision is low. Comprising the following steps: S1, collecting insect pest data and establishing a multivariable time sequence data set; s2, data preprocessing; s3, feature selection; and S4, establishing an insect pest prediction model, optimizing the insect pest prediction model by using a PSO module, and obtaining a prediction value of the number of insect pests at the next moment based on the optimized insect pest prediction model.

The invention relates to the technical field of agricultural information and seed and seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacles of peanuts. The method comprises the following steps: constructing initial digital twins of a land parcel, and synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system; historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters are fused, the system is driven to evolve into operation state digital twins, and seedling-soil-environment collaborative simulation is achieved. Evaluating the continuous cropping obstacle risk and the seedling adaptability based on the twinborn body, and generating a comprehensive diagnosis report containing a nutrient imbalance index, pathogenic bacteria abundance prediction, a root exudates cumulative effect and a seedling disease-resistant adaptation coefficient; according to the method, differentiated seed and seedling optimization, soil improvement and cultivation strategies are implemented, and management measures are dynamically optimized through real-time data feedback in a growth cycle. Through cooperative driving of digital twinning and seed and seedling cultivation, source prevention, control and intervention of continuous cropping obstacles are achieved.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention provides an unmanned aerial vehicle tea tree disease detection method based on morphological perception, and belongs to the technical field of agricultural information and computer vision. The method comprises the steps of firstly collecting tea garden images and constructing a data set; then, a target detection network embedded with a differentiable morphological sensor module is constructed, and the differentiable morphological sensor module extracts multi-scale shape features by using differentiable morphological operation; an adversarial learning mechanism is introduced in training, and the distinguishing ability of the model on disease and health areas is enhanced through a discriminator; after the training is completed, mining a difficult case sample based on the cosine distance between the morphological characteristics and the disease prototype vector, and carrying out supplementary training; and finally, integrating a plurality of models with optimal performance, and generating a final detection result through weighted reasoning. The method effectively strengthens the perception capability of the model for the subtle morphological characteristics of the diseases, solves the problems of low disease detection precision and insufficient difficult sample learning in a complex tea garden background, and remarkably improves the detection accuracy and robustness.

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.

An agricultural product carbon footprint acquisition and calculation method and system belong to the technical field of carbon emission calculation and agricultural informatization crossing, adopt an ecological pyramid intelligent architecture, and are composed of a data acquisition module, a data preprocessing and storage module, a carbon footprint calculation engine module, a traceability and safety module and an application service interface module. A'carbon footprint collection-accounting-evidence storage-identification-query 'full-chain quantitative management system is constructed, accurate collection of multi-source heterogeneous carbon source data is realized through cloud, edge and end cooperation, a modular carbon footprint calculation engine is combined with a localized factor database, CPU-GPU cooperative calculation and a digital twinning technology are adopted to realize accurate accounting and uncertainty quantification, and the carbon footprint calculation efficiency is improved. A CogVLM multi-mode model is innovated and improved to be embedded into agricultural carbon footprint field knowledge and a lightweight calculation engine, and deep fusion of visual perception and carbon footprint dynamic evaluation is achieved.

The invention relates to the technical field of agricultural information, and discloses a wheat basal stem rot identification method, system and equipment and a medium. The method comprises the following steps: acquiring a to-be-identified wheat image; obtaining a trained disease identification model; wherein a network structure adopted by the disease identification model adopts an edge texture sensing module to replace a part of C3k2 modules in a backbone network of a YOLOv11 network, and a channel attention module is embedded in a detection head of the YOLOv11 network; and inputting the wheat image into the disease identification model, performing feature extraction on the wheat image by using the edge texture sensing module in the backbone network, and performing prediction by using the channel attention module in the detection head to obtain a basal stem rot identification result of the wheat image.

The invention discloses an infrared corn drought identification method based on wavelet boundary enhancement, and belongs to the field of agricultural informatization and plant phenotype identification, and the method comprises the following steps: S1, obtaining an infrared image of a corn plant; s2, executing two-dimensional discrete wavelet transform to generate a boundary response diagram; s3, generating a binary mask according to the characteristics of the boundary response diagram to the blade edge; s4, connected domain analysis is executed, candidate leaves are obtained, and independent single-leaf masks are generated; s5, the small holes are removed, a mask is obtained, and the blade curvature is quantified; s6, constructing a multi-dimensional feature vector reflecting the curling characteristics of the blade; and S7, outputting the drought grade corresponding to the corn leaf through the multi-layer perceptron model. By adopting the method, automatic identification and early warning of the early-stage water shortage state of the corn are realized.

The invention discloses a tea processing big data analysis and quality tracing system and method, and relates to the technical field of agricultural informatization management. The tea processing big data analysis and quality tracing system is constructed, full-process data collection, real-time monitoring and accurate tracing from fresh leaves to finished products are achieved, key parameter data in the processing process are collected in real time through a sensor, key factors influencing the tea quality are extracted through an advanced data analysis algorithm, and the quality of the tea is accurately traced. According to the method, the quality of tea leaves can be accurately identified, quality abnormal points are accurately positioned, the problems of incomplete data acquisition, lagged analysis and inaccurate tracing in a traditional method are solved, meanwhile, a unique tracing code is generated for each processing batch, a full-process data mapping table is constructed, dynamic updating and real-time query of quality information are realized, the quality management level of tea leaf processing is remarkably improved, and the method is suitable for large-scale popularization and application. The requirements of consumers on transparency and high quality are met.

The invention, which relates to the technical field of agricultural information, discloses a cotton high temperature resistance prediction system using multi-source data fusion, comprising a data acquisition module, a data processing module, a model construction module and a decision output module. The data acquisition module is used for acquiring multi-source heterogeneous data of a target area. According to the cotton high temperature resistance prediction system applying multi-source data fusion, through deep fusion of multi-source heterogeneous data and construction of a dynamic knowledge graph, accurate description of an environment-crop-soil complex interaction relationship under cotton high temperature stress is realized. A physiological development time driven mechanism model and an LSTM-XGBoost data driven model are innovatively coupled, a multi-scale prediction system formed from organ development to yield quality is established, and the dynamic cumulative influence of high temperature on the cotton shedding rate, yield composition and fiber quality can be quantified. The problems of inaccurate early warning and insufficient prediction dimension caused by single data and mechanism deficiency of a traditional method are effectively solved.

The invention relates to the technical field of digital twinborn and agricultural information, and discloses a digital twinborn growth simulation and optimization method for safflower seedling breeding, and the method comprises the following steps: constructing individual digital twinborn bodies fusing genotypes, phenotypes and multi-dimensional environmental perception data; performing hybrid modeling in combination with the mechanism model and a depth time sequence neural network, and correcting growth prediction deviation in real time; and based on multi-objective optimization of biomass, medicinal component content and water efficiency, generating a precise cultivation regulation and control instruction. The system comprises a data acquisition unit, a twin construction unit, an environment fusion unit, a hybrid modeling unit, a visualization unit, an optimization decision unit and an instruction execution unit. According to the method, through closed-loop intelligent regulation and control, the yield, quality and resource utilization efficiency of safflower seedling breeding are remarkably improved.

The invention discloses an AI-based rice planting environment data informatization management system, and relates to the technical field of agricultural informatization. The method comprises the steps that an environment data acquisition module obtains multi-dimensional space-time marked environment parameters; the disturbance modeling module is used for analyzing environment change, identifying a disturbance structure and calculating a sudden change value; the growth stage parameter module is used for constructing a stage characteristic parameter set; the growth state modeling module is used for generating growth state data in combination with disturbance and a growth stage; the strategy generation module formulates a resource regulation and control strategy based on the growth state and the feedback information; the resource coupling analysis module is used for identifying coupling and interference relations among resources; the strategy constraint module is used for adjusting a resource strategy to generate a control strategy; and the control execution feedback module is used for executing the regulation and control instruction and feeding back growth data. Through high-precision space-time environment data fusion, deep coupling analysis of environment disturbance and growth states and resource regulation and control closed-loop optimization management, intelligent and precise monitoring and regulation and control of a rice planting environment are realized.

The invention belongs to the technical field of agricultural informatization, and particularly relates to a non-destructive testing method and system for the preharvest citrus sugar degree based on a hyperspectral image. The method comprises the following steps: acquiring hyperspectral image data and sugar degree data of pre-harvest citrus samples; based on an improved CNN-FA-RB model, constructing a sugar degree detection model; constructing a training data set based on the hyperspectral image data and the sugar degree data, and training the sugar degree detection model by using the training data set; and based on the trained sugar degree detection model, realizing nondestructive detection and spatial distribution visualization of the pre-harvest citrus sugar degree. According to the method, an improved deep learning model is used, nondestructive testing of the preharvest citrus sugar degree in the outdoor orchard environment is achieved, and the method can be applied to preharvest citrus maturity judgment and analysis.

The invention discloses a high-standard farmland scene recognition method based on a deep neural network, and relates to the technical field of remote sensing image processing and agricultural information, and the method comprises the steps: 1, constructing a high-standard farmland scene sample library with prior significance, the high-standard farmland scene sample library comprises high-standard farmland sample features and high-standard farmland sample scale quantities; 2, constructing a high-standard farmland scene recognition model based on multi-source data fusion; 3, predicting a result, and outputting a scene category graph; according to the high-standard farmland scene recognition method based on the deep neural network provided by the invention, the problem that the prior art stays at a pixel level or an object level, depends on a single threshold value or shallow learning and has a semantic gap is solved.

The invention provides a multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention discloses an apple industry chain multi-source heterogeneous data coupling prediction and early warning method and system, and belongs to the field of agricultural information technology and intelligent prediction.The method comprises the steps that price data, weather data, Internet of Things sensor data and inventory data of an apple industry chain are collected, and vectorization is conducted after cleaning and dynamic time warping alignment; calculating a disaster influence factor alpha and a price conduction time lag tau based on the disaster event; inputting the multi-source data vector into a prediction model fused with bidirectional LSTM, multi-head cross attention and disaster sensing gating modules, and outputting a price prediction sequence; the invention aims to solve the problems of single data, lagging disaster response and the like in the prior art.

The invention belongs to the technical field of remote sensing image processing agricultural information, and particularly relates to a DS-xNet-based multi-source remote sensing time sequence data cultivated land utilization current situation extraction method. According to the method, the cultivated land and the water body can still be reliably recognized under the cloud or fog condition by combining Sentinel-1 and Sentinel-2, missing detection and misjudgment caused by cloud shielding are reduced, sNET captures short-term dynamic conditions, and mNET matrix memory enhances long-term dependence modeling, so that the recognition precision of seasonal and interannual changes is improved, supervision is applied to a branch and fusion layer, and the recognition accuracy of the cultivated land and the water body is improved. According to the method, each branch can learn independent discrimination capability and can cooperatively improve fusion output, over-fitting is reduced, generalization capability is improved, training convergence is accelerated, channel splicing retains discrimination information of each modal in a fusion stage, modal information conflict or loss caused by direct early fusion is avoided, classification accuracy is improved, and classification efficiency is improved. Various optical and radar indexes are combined and used to enhance the distinguishing capability of paddy fields, irrigation areas, different crops and non-cultivated areas.

The invention discloses a crop physiological status real-time diagnosis system and method based on multi-modal sensing, and belongs to the technical field of intelligent agriculture and agricultural information. The system comprises a multi-mode sensing module, a data fusion processing module and an intelligent diagnosis and decision module. The multi-mode sensing module synchronously collects crop morphology, physiology and environment data; the data fusion processing module performs preprocessing and feature extraction on heterogeneous data; the intelligent diagnosis and decision module realizes multi-source information cross validation through a special diagnosis model, and generates a stress distribution diagram and a variable operation prescription; the method is based on the system, and leap-over diagnosis from appearance to mechanism is realized through multi-modal data acquisition, fusion processing and intelligent diagnosis; according to the invention, the industrial problem of high misjudgment rate of a single information source is solved, early, accurate and in-situ diagnosis of the crop physiological status is realized, and an intelligent equipment solution integrating perception and decision is provided for precision agriculture.

The invention relates to the technical field of agricultural information, in particular to a vegetable planting mode intelligent recognition system and method based on stable isotopes. The system collects stable isotope data of delta 13C, delta 15N and the like of vegetables, constructs a multi-dimensional database containing production places and cultivation modes, integrates four machine learning models of PLS-DA, SVM, RF and ANN to realize automatic identification of the planting modes, and realizes user interaction through electronic map visualization sampling points, background support data uploading and model dynamic training and front-end applets. Innovation points comprise stable isotope and machine learning fusion, a multi-model layered recognition architecture, an incremental learning mechanism, geographic information visualization and the like. The system can efficiently and accurately identify the vegetable planting mode, and the agricultural supervision efficiency is improved.

The invention relates to the crossing field of agricultural information technology and urban sustainable development, and discloses an urban micro-agriculture three-dimensional edible landscape intelligent low-carbon benefit evaluation and adjustment and optimization method and an urban micro-agriculture three-dimensional edible landscape intelligent low-carbon benefit evaluation and adjustment and optimization system. According to the method, data are collected through a multi-modal behavior sensing device and a plant physiology monitoring array, a behavior-ecology synchronous time sequence set is constructed, a plant growth dynamic simulation model fusing behavior disturbance factors is established, carbon sink, energy consumption and food output efficiency are quantified, and a plant growth dynamic simulation model is established. And based on a multi-target particle swarm optimization algorithm, generating a spatial layout and interaction strategy combined adjustment and optimization scheme. The system comprises a behavior perception module, an ecological monitoring module, a space-time alignment module, a model simulation module, a benefit calculation module and a visual pushing module. According to the method, the low-carbon benefit evaluation precision and the community participation sustainability are remarkably improved, the actual measurement prediction error is reduced by 42%, and the comprehensive benefit is improved by 28%.

This invention relates to the fields of agricultural information processing and artificial intelligence, specifically to a method and system for predicting farmland yield based on heterogeneous graph neural networks. The method includes: acquiring multi-source farmland data and extracting initial feature vectors from farmland plot nodes; constructing a heterogeneous graph structure containing multiple semantic relationships based on the initial feature vectors; updating node features of the heterogeneous graph structure using a heterogeneous graph neural network, dynamically aggregating information from multiple types of neighbor nodes through a relationship-aware message passing and edge propagation gating mechanism; enhancing node features using a spatiotemporal dependency reinforcement mechanism and a knowledge-guided reasoning mechanism; and outputting regression prediction results for farmland yield through a prediction module based on the enhanced node features. The aim is to achieve modeling and intelligent yield prediction based on multi-source heterogeneous data in agricultural systems, improving the environmental adaptability, prediction generalization ability, and interpretability of farmland yield prediction.

A system is used to perform systems modeling related to at least agriculture. The system can include a memory unit that stores executable instructions wherein the instructions can be used to perform systems modeling. The system can obtain, store, and use historical data related to agricultural scenarios. The system can further generate and / or display a human machine interface wherein a user can enter input to tailor the input data based on the preferences and / or assumptions of the user. The system is further configured to perform a simulation based on the user-defined input data. The system can then provide and / or display the results of the simulation as output data The output data can include predicted and / or projected outcomes based on the input data wherein said outcomes can include agricultural information, market information, environmental information, and / or farm management information. The disclosure allows for accurate modeling of complex systems.