Yield Prediction System Research Articles

SMART-CYPS: an intelligent internet of things and machine learning powered crop yield prediction system for food security

SMART-CYPS: an intelligent internet of things and machine learning powered crop yield prediction system for food security

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

AbstractIn-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

Satellite Imagery, Big Data, IoT and Deep Learning Techniques for Wheat Yield Prediction in Morocco

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0,00036 and 0,99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc, using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.

Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing

Precision agriculture is a technology-driven approach to farmer to improve their crop yields and reduce costs. One of the major challenges facing farmers today is the lack of precise prediction which leads to decreased production and mismanagement of labour and resource. Precision technology is costly, and they only rely on manual observations which are less precise. Crop yield prediction systems on cloud computing can solve both problems by predicting the harvested fruit at earlier stages of farming and ease farmers to make decisions. In this study, we proposed a crop yield prediction system for farmers that utilizes cloud computing and machine learning techniques. The system uses data on the physical growth of the plant such as plant’s height at 15 and 30 days after transplant, type of pollination treatment, condition of the leaves, and their variety to predict the crop yield at the early stage. Logistic regression, k-nearest neighbour, and random forest classifier were used to compare the accuracy of the model. Our result shows that by using a random forest classifier, it can achieve an accuracy of 91% which is higher than logistic regression which is only 73% of accuracy, and k-nearest neighbour with 82% accuracy. The study highlights the potential of precision agriculture, cloud computing, and machine learning to revolutionize the way farmers manage their crops and increase their efficiency and productivity, even with the limited resources and hardware that many farmers have.

Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system

ObjectiveThe worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data. LimitationA significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach. MethodInitially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR). ResultA simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture. ConclusionThe study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.

OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN

Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.

Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia

<p>This study aimed to explain the crop yield prediction system as a way to address the challenges posed by global warming and climate change in Saudi Arabia, while also taking into account socio-economic factors. Machine learning models were trained using crop yield prediction data to provide recommendations for future crop production. Climate change poses significant challenges, with rising temperatures and extreme weather events being increasingly evident. Agriculture, contributing 14% of greenhouse gas emissions, plays a crucial role in exacerbating this issue. This study introduced a crop yield prediction system leveraging machine learning models trained on comprehensive datasets. Recommendations derived from these models offer insights into optimal crop rotation strategies, particularly relevant for regions like the Kingdom of Saudi Arabia. Collaboration between farmers and governments, informed by data-driven approaches, is crucial in this endeavor. Utilizing a customized dataset, this study analyzed a machine learning model performance and identified optimal hyperparameters. XGBoost ensemble emerged as the top performer with an R<sup>2</sup> score of 0.9745, showcasing its potential to advance crop yield prediction capabilities. By integrating machine learning into agricultural decision-making processes, stakeholders aim to enhance crop production and soil health and contribute to climate change mitigation efforts. This collaborative effort represents a significant step toward sustainable agriculture and climate resilience in Saudi Arabia.</p>

Intelligent crop yield prediction system using neural networks and databases

Machine learning plays an important role in decision support and yield forecasting. This is an effective tool for determining strategies during the growing season of plants. The article proposes a method for predicting yield using a complex system consisting of a convolutional neural network (CNN), a feedforward neural network (FNN), and a SQLiteStudio database. The system includes several stages of data processing, starting with the collection and analysis of images and digital data obtained from various sources, and ending with yield forecasting based on this data. A convolutional neural network (CNN) is used to analyze images and video streams to recognize and count fruits on trees, providing accurate data about the status of the crop. Feedforward neural network (FNN) is used to analyze digital data, such as weather station data and long-term crop yield data, to subsequently predict crop yields. The received data is stored in a relational database, which ensures their structured storage and access for subsequent processing. Used SQL language to perform various database operations. To automate the process of counting fruits on trees, the YOLOv8 convolutional neural network model is used, which allows recognizing objects in real time. A Python script has been developed to process images using YOLOv8 and save the results to a database. An integrated system combines various methods and technologies to predict yields and automate data collection and analysis processes. The developed model showed a mean square error (MSE) of 7.33 and a mean absolute percentage error (MAPE) of 6.27%.

XGBoost sequential system for the prediction of Persian lemon crop yield

AbstractToday, Mexico ranks second worldwide in total lemon production; however, the yield per hectare is only ranked eighth, which makes evident a low productive potential in the country. This work provides an impact analysis of certain physical and climatic variables on the yield of Persian lemon (Citrus latifolia) harvest; its objective is to determine which variables are controllable to guarantee a higher crop yield in different uncontrollable climatic conditions. For this, we developed a yield prediction system. The system consists of three sequential Extreme Gradient Boosting (XGBoost) algorithms fed with historical information, which allow for prediction of Persian lemon crop in its three stages: flowering, budding, and fruiting, having as input variables planting density, number of trees in production, pruning and cleaning, and pest control, in addition to uncontrollable weather elements such as rain and wind. The variables with the greatest impact on crop yield are pruning, cleaning, and pest control. The algorithms have a performance of 0.87, 0.93, and 0.97, respectively, for each stage. The analysis provides the farmer with a tool that allows him to make data‐driven decisions based on the manipulation of good agricultural practices considering the current weather conditions, thus promoting a higher harvest yield and thus a higher profit margin.

Energy crop yield simulation and prediction system based on machinelearning algorithm

Energy crop yield simulation and prediction system based on machinelearning algorithm

Improved information dissemination services for the agricultural sector in Thailand: development and evaluation of a machine learning based rice crop yield prediction system

The upward surge in world population, sustainability, and concerns about food and nutritional security place the food and agricultural sector amidst challenges. These challenges encompass increasing food supply, meeting higher quality standards, and enhancing productivity and crop yield prediction. This study discusses the development and evaluation of a Machine Learning (ML) based rice yield prediction system. Utilizing extensive historical datasets from the Office of Agricultural Economics (OAE), Ministry of Agriculture and Cooperatives (MOAC), Thai Meteorological Department, and Department of Internal Trade of Thailand, all pivotal variables at the national rice level were considered. Examining ML models like the Generalized Linear Model (GLM), Feed-Forward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest (RF), the study proposes a Web-based system to disseminate Thailand's rice information and yield predictions, aiding decision-making processes. Beyond evaluating the performance of each prediction model, user satisfaction was scrutinized. Results reveal that the FFNN, a deep neural network, adeptly handles complex nonlinear relationships in high-dimensional datasets. Despite the FFNN's longer training runtime due to Big-O complexity, it exhibits the shortest execution time for predictions. System usability assessment, based on ten standardized questions, indicates participants found the proposed system marginally acceptable, reporting positive user experiences and feeling confident in system use without perceiving it as inconsistent or cumbersome. This study is significant for enhancing agricultural information dissemination services across diverse sectors, benefiting farmers, wholesalers, retailers, and policymakers.

Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence

Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, and costly, and its accuracy depends on the scale of the sample. To overcome these problems, hybrid approaches of computer vision, deep learning (DL), and machine learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally, a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy up to 94.81% and yield prediction accuracy up to 99.58%.

Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning

Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R2 score of 98.82% and an accuracy of 99.67%.

Maize (Zea Mays L.) Yield Estimation Using High Spatial and Temporal Resolution Sentinel-2 Remote Sensing Data

ABSTRACT Maize (Zea mays L.) is one of the world’s most important annual cereal crops and its yield can be estimated for a wide variety of purposes. The objective of this work is to evaluate in which stage of crop the best fit between remote sensing data and real yield occurs to predict yield in corn seed crops. For this, polynomial regression models were used between spectral indices of vegetation and real yield in 10 days time’s windows covering the critical period for generation of performance. Subsequently, the predictive capacity of the best goodness of fit model was evaluated by comparing estimates with those made using a conventional field estimation method. This experiment was carried out in production fields located in Tandil and Loberia district inside of the Argentine Pampas Region in southeast of Buenos Aires province in summer (from january to march) of 2020. We found the highest level of adjustment between vegetal index and real yield (R2 = 0.91) in the time window of 110 to 120 days after sowing (DAS) corresponding to the end of the critical period. Then, the predictive performance was evaluated, satellite model shows an underestimation of 53 kg/ha (0.72% relative error) while the conventional method underestimated by 955 kg/ha (13% relative error). A close relationship between remote sensing data and grain yield at the end of the critical period of maize can be evidenced, and this information can be used to predict yield early in the southeast of Buenos Aires province. Using the methodology here developed it is recommended to analyze- time series of satellite vegetal index in maize crops in other regions and climates to make more robust the yield prediction system.

Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania

Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops.

Optimized Deep Learning Methods for Crop Yield Prediction

Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep belief network with Visual Geometry Group (VGG) Net classification method over the tweak chick swarm optimization approach to estimate agricultural production. The Network’s successively stacked layers were fed the data parameters. Based on the input parameters, a crop production prediction environment is constructed using the network architecture. Using the tweak chick swarm optimization technique, the best characteristics of input data are preprocessed, and the optimal output is used as input for the classification process. Discrete Deep belief network with the Visual Geometry Group Net classifier is used to classify the data and forecast agricultural production. The suggested model correctly predicts crop output with 97 percent accuracy, exceeding existing models by maintaining the baseline data distribution.

CROP YIELD PREDICTION USING MACHINE LEARNING ALGORITHMS

India is an absolute agriculture country whose economy is mainly depends on agriculture yield growth which have been essential to feed a growing population while reducing the environmental impact of food production at the same time. The impact of climate change for most of the agriculture crops affects the yield. It attempts to solve the issue by building a prototype of an interactive predictive system which predicts the yield of certain suitable crops. The primitive aim of this study is to come up with an efficient crop yield prediction system using Kalman filter algorithm. A farmer can put any verity of crop in his field, but there can be no clarity of how much yield he might get and what’s the maximum profit he can gain. Technology can help them to predict the yield of the crop considering various factors whether to go for that crop or not. Crop yield is the proportion of harvest delivered per area of land. It's a significant measurement to comprehend in light of the fact that it assists us with figuring out food security and furthermore makes sense of why your potatoes can cost more one year and afterward less the next year. In this project, we will study different methods that can be used to predict the crop yield. This study shows that they can also be done using Machine Learning algorithms known as Kalman Filter Algorithm and Random Forest Algorithm. In this Project, we compare various Machine Learning algorithms version and different Data processing techniques to obtain best possible results in crop yield prediction. To get the highly accurate percentage to predict yield is by using Kalman filter and Random Forest Algorithm.

CROPUP – A Crop Yield Prediction and Recommendation System with Geographical Data using DNN and XGBoost

Agricultural management is significant in a populous country like India. Farmers must have advance knowledge about predicted crop production and crop condition within particular area to make economic and farming decisions. To generate yield, we consider factors like temperature, humidity, pressure, NDVI values, Latitude, Longitude etc. When cultivating a particular crop on a specific type of soil, there are a number of factors to be considered. A crop recommender system considers soil properties such as N, P, and K, as well as other factors like rainfall, humidity, and pH levels, to choose the best crop for the farm. This paper presents a predictive algorithm that would estimate crop yield using deep neural networks with geographical data. A recommendation system was built using machine learning algorithm like Xgboost to recommend the suitable crop. A user interface named CROPUP has been developed to scale up crop productivity and efficiency using the proposed algorithms.

A crop variety yield prediction system based on variety yield data compensation

Forecasting crop variety yield, selecting suitable ecological regions for designated crop varieties, and finding high-yield crop varieties for designated ecological regions are all critical aspects of crop variety popularization. A crop variety yield prediction system based on variety yield data compensation (CVYPS-VYDC) is described in this paper. This novel system is used to predict the yield of designated crop varieties using the large amount of breeding data stored in the Golden Seed Breeding Cloud Platform and meteorological data from the National Meteorological Science Data Center. The prediction system was devised to (a) forecast the yield of designated crop varieties in the target ecological regions, (b) calculate the effects of different environmental factors on the yield of different crop varieties, (c) identify the key growth periods affecting crop yield, and (d) promote crop varieties according to a comprehensive analysis of the promoted variety yield, preferences for different meteorological factors, and climatic characteristics of the target ecological regions. In addition to meteorological factors, soil factors affect crop yield. However, large-scale measurements of soil composition require a large number of sensors and considerable human resources. By using the yield data compensation method, the yield data of predicted varieties across different regions were expanded and corrected, which reduces the yield prediction error caused by soil differences among regions to some extent. In addition, the coefficient of determination (R2) between actual and predicted yield was improved to 0.82. In addition to predicting the yield of 90 maize varieties in the Huang-Huai-Hai area of China, the preferences of different maize varieties for different environmental factors were analyzed at the same time. CVYPS-VYDC identified the key environmental factors greatly impacting the maize yield, and the proportion by which different factors affected the yield prediction of different maize varieties was determined. Moreover, the growth periods that have great impact on maize yield were identified by analyzing 39 cultivation sites of various maize varieties in the Huang-Huai-Hai area of China.

Moving climate seasonal forecasts information from useful to usable for early within-season predictions of durum wheat yield

Crop models fed by seasonal forecasts to deliver yield forecasts are becoming a valuable tool to tackle food production shocks and food price spikes triggered by climate change and extreme events. However, to what extent seasonal climate prediction can be coupled to crop models in an effective climate service to provide durum wheat yield forecasting at local, regional and national scales remains unknown. Within the H2020 MED-GOLD project, based on participatory action research and through a co-designing approach, a complete assessment of raw and bias-adjusted ECMWF-System5 seasonal forecasts data in feeding the Delphi model was performed and explicitly compared with a previous version of the Delphi model fed by a historical scenario, both benchmarked with yield observation data. The 5 and 3-months lead prediction accuracies of year-to-year variations in durum wheat yield in Italy for 31 crop years were explored and evaluated for which techniques to remove biases from input data to adopt and for which areas they performed better. The raw seasonal forecast (BC0) showed better yield forecasting correlation than the historical scenarios (0.42 vs 0.17) in southern Italy, but not better accuracy (53 % for both), while a first bias-adjusted seasonal forecast (BC1) had again a positive effect in terms of correlation (r = 0.43) especially in southern Italy and generally in terms of accuracy (59 %). Finally, by the implementation of a second bias-adjusted seasonal forecast (BC2) the best yield prediction system was reached, especially for the areas of central and southern Italy with a total accuracy of 62 % and r = 0.52.