Crop Forecasting Research Articles

An IoT-Enabled Real-Time Crop Prediction System Using Soil Fertility Analysis

Changes in soil fertility have led to a decline in crop production, making it challenging for farmers to select the best crops based on soil conditions. Accurate crop prediction can significantly enhance crop productivity, and machine learning plays a crucial role in this process. Crop forecasting is influenced by soil, geographic, and environmental characteristics, with feature selection being essential for identifying suitable crops. In this study, we developed a real-time soil fertility analyzer to obtain the real-time values of soil parameters such as potassium, phosphorus, nitrogen content, temperature, pH, moisture content, and electrical conductivity. The crops examined were coconut, ginger, plantain, and tapioca. The data collected from this analysis served as the dataset for different training and testing classification algorithms for crop prediction using 100 soil samples. Among the algorithms tested, the k-nearest neighbors (KNN) algorithm demonstrated the highest performance, with an accuracy of 84%, precision of 85%, recall of 88.8%, and specificity of 92.4%. These results indicate that machine learning, combined with real-time soil analysis, can effectively predict suitable crops, enhancing crop productivity and aiding farmers in making informed decisions. This approach can revolutionize traditional farming practices by providing precise, data-driven insights into crop selection, ultimately improving agricultural efficiency and sustainability.

DIGITAL TOOLS FOR MONITORING AND MANAGEMENT IN AGRICULTURAL PRODUCTION

The digitalization of agricultural production has led to the widespread use of mobile technologies for monitoring and managing agricultural processes. Applications and devices such as drones and satellites allow you to receive real-time data on soil conditions, humidity, plant growth, and pest threats. This data helps farmers to make quick decisions on irrigation, fertilization, and crop forecasting. Modern mobile solutions such as AgroMonitor integrate with various data sources and provide accurate information, which increases the efficiency of resource management and reduces production risks. The introduction of these technologies not only helps to increase yields but also ensures the sustainable development of agricultural operations, minimizing the impact of external factors. Thus, digital tools are becoming an important element of the modernization of the agro-industrial sector, contributing to its competitiveness at the global level.

MResGat: Multi-head Residual Dilated Convolution Assisted Gated Unit Framework for Crop Yield Prediction

The importance of predicting crop yields has increased due to growing concerns of surrounding food security. Early forecasting of crop yields holds a pivotal role in avoiding starvations by estimating the food supply available for the expanding global population. Several Deep Learning (DL) and Machine Learning (ML) techniques are involved to develop effective and accurate crop yield prediction model. Nevertheless, existing models faces some limitations such as less accuracy, high error rate because of noisy data, high training time and extracted less effective features for prediction. To overcome these issues, the novel DL methodology is introduced for attaining high accurate crop yield prediction. Initially, the soil, weather and other resources big data are collected from the various agriculture field. In data collection phase, the input data of larger size are stored in the Hadoop platform for the purpose of storing as well as processing the entire data in a distributed manner. The data are pre-processed through the utilization of Missing value imputation and z-score based data normalization. From the pre-processed data, the optimal features are considered using Integrated Correlation Random recursive elimination (InCorRe) approach. Based on the previous soil and weather information, the suitable yield of crops can be predicted using Multi head Residual dilated convolution assisted gated unit (MResGat) model. Finally, the losses of the network model can be optimized using African vulture optimization algorithm (AVO). The proposed method is evaluated using the several performance metrics, which achieved 0.023% of MSE value and 0.036% of MAE values.

On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN.

DEVELOPMENT AND APPLICATION OF AN INTEGRATED INFORMATION MODEL FOR OPTIMIZING LAND USE AND FORECASTING YIELDS IN AGRICULTURAL PRODUCTION

The paper presents the development of a conceptual information model for optimizing land use and crop forecasting in agriculture, implemented using the PHP programming language, Javascript and the MYSQL database management system. The model is designed to solve optimization problems in various fields such as logistics, manufacturing, finance, etc. As part of the work, an information model was developed, which is a set of data, algorithms and software modules designed to solve optimization problems. The model is implemented based on PHP, Javascript, and MYSQL technologies. The model allows you to solve optimization problems of varying complexity using various optimization methods such as linear programming, nonlinear programming, artificial intelligence methods, etc. The model can be applied to solve optimization problems in various fields. In addition, the model allows you to solve optimization problems with a high degree of accuracy and in a short time, and also has a user-friendly interface that allows users to easily and quickly set optimization tasks. An information model has been developed that will make it possible to predict the harvest taking into account data on weather, soil, crops and historical yield data, as well as optimize land use taking into account data on soil type, climate, crop rotation and financial indicators. The developed information model can improve the efficiency and sustainability of agricultural production. The model can be used by agronomists and farmers to make more informed decisions. The model takes into account data on soil type, climate, crop rotation and financial indicators to optimize land use.

Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. Despite significant advancements in crop yield forecasting, existing methods often struggle with integrating diverse sensor data and achieving high prediction accuracy under varying environmental conditions. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms, namely random forest (RF), partial least squares (PLS), ridge regression (RR), k-nearest neighbor (KNN) and extreme gradient boosting decision tree (XGboost), were utilized for multi-sensor data fusion, together with three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average), for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB + MS + Texture + TIR combination demonstrating the highest fusion performance (R2 = 0.660, RMSE = 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the accuracy of wheat yield prediction. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE = 0.668 t ha−1). It significantly outperformed both the second-layer ensemble methods of stacking (R2 = 0.668, RMSE = 0.673 t ha−1) and feature-weighted (R2 = 0.667, RMSE = 0.674 t ha−1), thereby exhibiting superior predictive capabilities. This finding highlighted the third-layer ensemble method’s ability to enhance predictive capabilities and refined the accuracy of wheat yield prediction through simple average ensemble learning, offering a novel perspective for crop yield prediction and breeding selection.

Research Paper On Maximizing Crop Forecast Accuracy Through The Analysis of Soil Composition and Weather Patterns.

Research Paper On Maximizing Crop Forecast Accuracy Through The Analysis of Soil Composition and Weather Patterns.

Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts

Abstract. The availability of coherent time series of crop statistics is essential for better analyzing the past, understanding the present, and predicting future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and that are sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. However, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the EUROSTAT REGIO database – subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower, and sugar beet. All data are harmonized towards the hierarchical structure of the EUROSTAT legend and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344 282 records is presented here (115 974 for wheat, 122 705 for barley, 35 274 for grain maize, 34 916 for sugar beet, and 35 413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time-span range of 21 years. A flagging system details, for each data record, information on data sources, processing steps, and quality-checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning. We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at the EU level are briefly discussed. The dataset is accessible with European Commission Authentication Service (ECAS) login at https://doi.org/10.2905/685949ff-56de-4646-a8df-844b5bb5f835 (Ronchetti et al., 2023b).

Application of geoinformation systems in the agricultural complex

Precision farming, encompassing coordinated farming, adaptive landscape farming, and precision farming, represents a scientific paradigm in agricultural production that capitalizes on the inherent variability in soil fertility within localized areas. By tailoring practices to specific soil characteristics, precision farming optimizes profitability through the targeted application of fertilizers and plant protection products. Serving as the epitome of flexible landscape farming, precision farming integrates advanced agricultural technologies to achieve heightened productivity levels. Data collected through precision farming practices informs critical aspects of agricultural management, including strategic planting, precise calculation of input quantities, improved crop forecasting, and enhanced financial planning. Successful implementation of precision farming hinges on a thorough consideration of local soil attributes and climatic conditions, underscoring the importance of site-specific adaptation in modern agricultural strategies.

ARIMA and ARIMAX Analysis on the Effect of Variability of Rainfall, Temperature on Wheat Yield in Haryana

The national and state governments require crop production forecasts to make a variety of policy decisions on import-export, storage, distribution, price, and other factors. This article presents a pre-harvest forecasting method specially developed for crops grown in the western region of Haryana (India). The western region includes Hisar, Sirsa, and Bhiwani districts. For crop forecasting in the Hisar, Sirsa, and Bhiwani regions ARIMA and ARIMAX models have been framed. For the development of the ARIMAX model, climate data during the growing season of the crop were used as input along with the crop yield. The percentage difference between the root mean square error and the wheat yield estimations determined by the real-time yield(s) indicates how well-competing models performed in terms of forecasting. The ARIMAX model performs well at all time points with a lower measurement error compared to the ARIMA model.

Harvest Prediction by Residents of Ukrainian Polissia: аn Agrometeorological Experience

Scientists of various fields of knowledge are always interested in the traditional experience of people in order to involve it in practical activities. All peoples have many signs by which they determine whether the year will be fruitful or not. This can be considered most consistently on the example of studying folk meteorology. The purpose of the publication is to identify general regional features and local peculiarities of crop forecasting by residents of Ukrainian Polissia as per weather phenomena, based on field materials collected during 2002–2019 in the northern districts of Volyn, Rivne, Zhytomyr, Kyiv, and Chernihiv regions and from emigrants from the Chornobyl exclusion zone using the author’s questionnaire. The methodology of the publication is based on general scientific principles and basic requirements that are put forward to works of historical and ethnological direction. To achieve the set goal, rational and irrational signs are used as predictors of the peasant’s well-being, in particular, observations of the first thunder, snow, hoarfrost, and icicles. An important criterion for prediction was the day of the week when snow or frost first fell. Attention is drawn to the Christmas holidays, which were a certain nodal point for predicting the next year’s harvest by inhabitants of Polissia. Phonetic coincidences of word forms when predicting the harvest are noticed. Since agriculture was the main form of livelihood for Ukrainians in Polissia, many meteorological signs aimed at predicting the future harvest with minor local features within the region have been preserved in folk practice. In this way, the practical possibility of using folk experience as indicators of the terms of field work and various agrotechnical measures is confirmed. The use of the positive experience of traditional agrometeorology (after appropriate verification by specialists) can be useful in modern agricultural practice, considering their general regional and local features.

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

Assessing cropland disagreement in Tanzania using machine learning methods with Sentinel-2 and Planet Scope imagery

ABSTRACT East Africa faces major land use pressures arising from the harsh effects of climate change and fast-increasing human populations. Recent efforts to increase food production in this region have focused on cropland mapping and approaches to improve yield. Generating accurate cropland maps provides critical support to inform policy, investment, and logistical decisions that address food security, but cropland disagreement assessment studies are lacking mostly due to the unavailability of fine-scale data. Understanding the inconsistent nature of pre-existing global cropland mapping products necessitates disagreement assessment as the most valuable urgent tool to align agricultural resource allocation and policy realignment to tackle food security challenges. This study evaluates cropland disagreement in Kilombero and Ulanga districts in Morogoro, Tanzania based on land use land cover (LULC) maps generated using machine learning methods and high-resolution PlanetScope and Sentinel-2 data sets acquired from 1 January 2017 to 31 December 2018. We created time-variant median composites and performed a three-step assessment; (i) generating multi-class LULC maps in Google Earth Engine, (ii) generating croplands and non-cropland LULC classes from the multi-class images, and (iii) assessing cropland disagreement based on GIS conditional statements. Results show minimal disagreement in mapping cropland based on higher-resolution PlanetScope and Sentinel-2. Results based on the Random Forests and Support Vector Machines relatively outperform those from Classification and Regression Trees achieving 93%, 89%, and 83% with PlanetScope and 91%, 86%, and 76% with Sentinel-2, respectively. On average, 73% of the pixels were consistently classified while 27% were misclassified between cropland and non-cropland classes. Prior unsupervised land cover cluster analysis may substantiate the quality of sampling for accurate land cover classification. Our results indicate that in data-sparse regions where confirmatory ground truth data are lacking, remotely sensed cropland identification may be more successful using Support Vector Machines on newer high-resolution imagery, thereby improving crop forecasts.

Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Agri Crop Recommendation System Using Suitable Machine Learning Techniques

In coastal states like Tamil Nadu, agricultural uncertainty lowers productivity due to a lack of modern technology, unstable climate changes, and a lack of good irrigation facilities. The ability to utilize modern technology in agriculture is mandatory in the current situation. Agricultural issues like crop rotation, crop forecasting, crop protection, water requirement, and fertilizer requirements can be solved using machine learning technology. It is possible to forecast and derive a precise model from the data using machine learning techniques. Agricultural crop recommendations based on productivity and season is proposed in this work. This might make it possible for prospective farmers to engage in improved agriculture. A farmer may receive a system of ideas to increase production and can move forward smoothly. Crops are advised based on quantity and weather concerns. The analysis of the crop dataset resulted in the recommendation of crops based on their productivity and growing season.

An open-source tool for improving on-farm yield forecasting systems

IntroductionThe increased frequency of extreme climate events, many of them of rapid onset, observed in many world regions, demands the development of a crop forecasting system for hazard preparedness based on both intraseasonal and extended climate prediction. This paper presents a Fortran version of the Crop Productivity Model AquaCrop that assesses climate and soil fertility effects on yield gap, which is crucial in crop forecasting systemsMethodsFirstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.ResultsResults revealed that the Fortran version showed statistically similar results to the original version (r2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.ConclusionAQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.

Implementation of Prediction of Crop Using SVM Algorithm

Abstract: Crop forecasting is the process of predicting the yield or production of crops for a given period based on historical data, weather and other relevant factors. The prediction can be used to inform crop planting, harvesting and marketing decisions. Machine learning and artificial intelligence techniques are increasingly being used to improve the accuracy of crop forecasting. These techniques use algorithms to analyze large amounts of data, such as weather patterns, soil conditions, and crop history, to predict future crop yields. Crop prediction models can be used by farmers, agribusinesses, and governments to optimize crop management, reduce waste and maximize profit. Accurate crop forecasting can also help mitigate the impact of climate change on agricultural production by enabling farmers to adapt to changing weather conditions and other environmental factors.

Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model

This study presents a novel hybrid model that combines two different algorithms to increase the accuracy of short-term berry yield prediction using only previous yield data. The model integrates both autoregressive integrated moving average (ARIMA) with Kalman filter refinement and neural network techniques, specifically support vector regression (SVR), and nonlinear autoregressive (NAR) neural networks, to improve prediction accuracy by correcting the errors generated by the system. In order to enhance the prediction performance of the ARIMA model, an innovative method is introduced that reduces randomness and incorporates only observed variables and system errors into the state-space system. The results indicate that the proposed hybrid models exhibit greater accuracy in predicting weekly production, with a goodness-of-fit value above 0.95 and lower root mean square error (RMSE) and mean absolute error (MAE) values compared with non-hybrid models. The study highlights several implications, including the potential for small growers to use digital strategies that offer crop forecasts to increase sales and promote loyalty in relationships with large food retail chains. Additionally, accurate yield forecasting can help berry growers plan their production schedules and optimize resource use, leading to increased efficiency and profitability. The proposed model may serve as a valuable information source for European food retailers, enabling growers to form strategic alliances with their customers.

Manfaat chatGPT dalam hubungan manusia dengan alam: menggunakan chatGPT di bidang pertanian

ChatGPT, a powerful language model, claims to be able to analyze data and provide actionable agricultural insights. According to him, this data can be used for crop forecasting, soil analysis, plant disease and pest identification, precision farming and irrigation scheduling. ChatGPT states that it can be used to generate accurate and timely reports, alerts, and insights that can help businesses make more informed decisions, improve results, and improve customer service. In this article the author explores the potential use of GPT chat in agriculture, according to him. The author admits asking chatGPT questions about its uses for agriculture. Some of its stated uses are possible now and some are potential for the future. The author has analyzed and edited GPT chat replies.

Optimizing Crop Yields through Machine Learning-Based Prediction

The application of machine learning techniques in agriculture, particularly in harvest forecasting, is gaining traction as a means of addressing this issue. The major project, "Optimizing Crop Yields through Machine Learning-Based Prediction," takes a comprehensive approach to this issue by considering a variety of parameters, including temperature, humidity, rainfall, and soil nutrient levels, to Figure out which crop is best to grow in those conditions. Naive Bayes, Random Forest, Support Vector Machines, Decision Trees, K-Nearest Neighbours, and Bagging, as well as feature selection methods like Synthetic Minority Oversampling Technique, Majority Weighted Minority Oversampling Technique, Random Over-Sampling Examples, and Recursive Feature Elimination, are used to accomplish this. High precision rates and improved forecast outcomes are the goals of these methods. Using machine learning techniques in crop forecasts, farmers can gain useful insights and make decisions based on data that increase crop production and overall agricultural productivity. This work demonstrates the potential of machine learning to address issues in agriculture and influence the sector's future.