Crop Yield Forecasting Research Articles

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.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical s of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

A Review on the Current Trends in Crop Yield Forecasting Using Artificial Intelligence

In the face of escalating global food demands and the increasing unpredictability of climate conditions, the importance of precise crop yield forecasting has never been more critical. This paper provides a comprehensive review of the current trends in leveraging Artificial Intelligence (AI) to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security. Our review covers a range of AI methodologies, including machine learning, deep learning, and hybrid models, that have been employed to predict crop yields with increasing accuracy. Recent advancements have demonstrated that machine learning techniques, such as support vector machines and random forests, are effective in modeling complex agricultural data sets with a notable degree of precision. However, deep learning approaches, including convolutional and recurrent neural networks, have started to outperform traditional machine learning models, owing to their ability to process large-scale spatial-temporal data from remote sensing and IoT-based agricultural sensors. We also explore the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies, providing enhanced accuracy and robustness in yield prediction under varying climatic conditions. Additionally, this review discusses the integration of AI with geographic information systems (GIS) and remote sensing technologies, which has significantly improved the spatial resolution of yield predictions. We highlight several key challenges that remain, such as data scarcity, the need for model generalization, and the integration of socioeconomic factors into yield prediction models. In conclusion, AI presents transformative potential for crop yield forecasting. By harnessing cutting-edge AI technologies and addressing existing challenges, significant strides can be made towards more sustainable and efficient agricultural practices. This paper aims to inspire continued research and innovation in this critical field.

Predicting Crop Yields in Indian Agriculture Using Machine Learning

Agriculture provides the backbone of India's economy both within and between. The paper below provides a brief insight into the creation of a fresh model is currently in progress to estimate the variability in the crop yields across various regions in India. Via this simple setting-based approach, such parameters as year, district, season and area can be used to predict the amounts of crop outputs for certain years. By employing sophisticated regression techniques, Kernel Ridge, Lasso, and ENet algorithms are used, to improve the yield prediction precision. Additionally, it implies a technique of Scaled Regression which is specifically designed to improve the accuracy of the data resulting as long-term predictions. Key terms: Crop yield forecasting, Lasso algorithm, Kernel Ridge algorithm, ENet algorithm, Stacked Regression.

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield (HRY), is less explored. This research collated crop-level HRY data across four seasons (2017/18–2020/21) from Australia’s rice-growing region. Models were developed using the XGBoost algorithm trained at varying time steps up to 16 weeks pre-harvest. The study compared the accuracy of models trained on datasets with climate data alone or paired with vegetative indices using two- and four-week aggregations. The results suggest that model accuracy increases as the harvest date approaches. The dataset combining climate and vegetative indices aggregated over two weeks surpassed industry benchmarks early in the season, achieving the highest accuracy two weeks before harvest (LCCC = 0.65; RMSE = 6.43). The analysis revealed that HRY correlates strongly with agroclimatic conditions nearer harvest, with the significance of vegetative indices-based features increasing as the season progresses. These features, indicative of crop and grain maturity, could aid growers in determining optimal harvest timing. This investigation offers valuable insights into grain quality forecasting, presenting a model adaptable to other regions with accessible climate and satellite data, consequently enhancing farm- and industry-level decision-making.

Annual winter crop distribution from MODIS NDVI timeseries to improve yield forecasts for Europe

Crop yield forecasts allow policy makers to anticipate market behaviour and regulate prices. Annual updates on which crops are grown where can improve crop yield forecast accuracy. Existing efforts to map crops across the European Union resulted in late-season map availability or short time series that do not meet forecasting requirements. We propose a new approach to retrieve annual winter crop maps and improve forecasting efforts by identifying pixels with dominant winter crop signals using moderate resolution imagery. These pixels are distinguished from summer crop signals based on their senescence date. When this date precedes the theoretical maturity date of a winter crop, expressed in GDD, the pixel is labelled as having a dominant winter crop signal. Our 2018 map accurately identified 77% and 83% of dominantly winter-crop area, when compared to farmers’ declaration data and a high-resolution crop map for Europe, respectively. While the resulting annual winter crop maps underestimated winter crop area, derived region-specific annual NDVI profiles better described winter crop phenology as compared to the use of static maps. Regression analysis between these regional NDVI profiles and statistical wheat yield data indicates that our annual maps help explain more yield variability than static maps, with an RMSE reduction of 3% for the EU27 as whole. The proposed approach is applicable to long historical timeseries and provides maps before the end of the agricultural season. Those maps positively impact crop yield description, notably in eastern, northern, and northeastern European regions.

Smart Agriculture Automation System using ML

Abstract: The "Smart Agriculture Automation System using Machine Learning" project envisages a future where farming transcends manual labor and embraces data-driven automation. In response to the escalating demand for food amidst a burgeoning global population, traditional farming methods are rendered insufficient. However, Smart Agriculture Automation Systems offer a transformative solution by enhancing crop production efficiency and maintaining yield quality. Climate changeinduced challenges such as erratic weather patterns and heightened pest attacks further underscore the necessity for innovation in agriculture. At the heart of this system lies real-time sensor data, enabling precision farming practices that optimize resource utilization while fostering environmental sustainability. Leveraging Machine Learning algorithms, the system not only predicts and mitigates agricultural challenges but also detects early signs of crop diseases and nutrient deficiencies. This proactive approach ensures higher yields minimizes manual tasks and minimizes wastage. The integration of IoT technology enables remote monitoring and seamless data exchange, unlocking capabilities such as precise crop prediction, weather forecasting, and fertilizer recommendations. By analyzing historical data alongside current environmental factors, these systems accurately forecast crop yields and offer insights for informed decision-making in planting and harvesting cycles. Smart Agriculture Automation Systems embody adaptability, sustainability, and accessibility, heralding a new era of intelligent and efficient modern farming. This project integrates the Internet of Things (IoT) sensors and an innovative marketplace to revolutionize the agricultural landscape. Through this system, we aim to empower farmers with data-driven insights,

Crop Yield Estimation using Improved Salp Swarm Algorithm based Feature Selection

Crop yield estimation is the art of yield prediction before harvest and it is essential for planning and making conclusive agricultural policies. The forecasting of crop yield is essential in optimal nutrient management, crop insurance, crop market planning and harvest management. However, the crop yield estimation is considered as a challenging task because of huge amount of abundant information exists in the crop data. Therefore, an effective feature selection is required to be developed for removing the redundant attributes. In this research, an Improved Salp Swarm Algorithm (ISSA) based feature selection for an effective crop yield estimation. The Opposition Based Learning (OBL) and Local Search Algorithm (LSA) are incorporated in the ISSA’s initialization and exploitation phase for selecting optimum feature subset. The selected features from the ISSA are used to enhance the classification using Modified Long Short Term Memory (MLSTM) classifier. The performance of the ISSA-MLSTM is analyzed using accuracy, precision, recall, F-score, Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The existing researches such as Ensemble approach and MLSTM are used to evaluate the ISSA-MLSTM. The accuracy of the ISSA-MLSTM is 99% that is high when compared to the MLSTM.

Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets

This research uses a variety of machine learning models and exploratory data analysis (EDA) to forecast crop yields using USDA information from 2003 to 2013 in an effort to achieve precision agriculture. Not only did we want to predict agricultural output, but we also wanted to identify the underlying factors that affect yield. By means of thorough EDA, which encompassed a wide range of agricultural data, including weather patterns and USDA-sourced soil composition, we were able to gain important insights into the variables that impact differences in crop output. The thorough investigation that followed served as the basis for our machine learning modelling. We thoroughly assessed and contrasted the performance of a variety of machine learning algorithms, including Bagging Regressor, KNN, Decision Trees, Gradient Boost, Random Forest, and Linear Regression. The accuracy of the models varied noticeably, as the results showed: the Random Forest, Decision Trees, and Bagging Regressor models showed great accuracy, with respective values of 98.56%, 97.62%, and 98.59%. Conversely, KNN and Linear Regression showed reduced accuracy, indicating their limits in this situation. The robustness of our results was further improved by applying k-fold cross-validation, highlighting the significance of model validation in crop yield prediction. Some models showed changes in accuracy during cross-validation, which revealed more about their dependability. In addition to providing a thorough investigation of the variables affecting agricultural productivity, this study highlights the diverse forecasting powers of machine learning models. Our findings provide a path for well-informed agricultural decision-making by utilizing technology to optimize crop production estimates. The ultimate goal of this research is to support stakeholders in optimizing agricultural productivity and enable sustainable practices.

An Emerging Era Of Research In Agriculture Using AI

AI-driven precision agriculture, predictive analytics, robots, and market intelligence boost contemporary agriculture's production, efficiency, and sustainability. Precision agriculture, powered by AI algorithms, gives farmers detailed insights into crop health, soil conditions, and weather patterns for data-driven resource allocation and management. AI in agriculture's predictive analytics helps stakeholders forecast crop yields, market dynamics, and climate-related dangers, improving resilience and strategic planning. AI has great promise to solve agriculture's complicated problems. AI technologies allow computers to mimic human cognition and evaluate massive volumes of data to draw conclusions. AI can improve resource utilization, productivity, decision-making, and environmental effect in agriculture. AI-powered precision agriculture, crop monitoring, supply chain optimization, and market analysis are making agriculture more sustainable and resilient. To show AI's influence on farming, we explored precision agriculture, predictive analytics, robots, and supply chain optimization. Farmers may optimize resource usage, manage risks, and make data-driven choices using these tools, enhancing output, sustainability, and resilience.

Multi-Model Ensemble Approach for Soybean Crop Yield Estimation (Kharif-2023) in Latur District at Macroscale level

Crop area estimation is a critical aspect of agricultural monitoring and management, providing essential information for decision-making in the agricultural sector. Study was carried out at Semantic Technologies and Agritech services Pvt. Ltd., GIS and Remote sensing team, Pune during Kharif-2023. All methodology given by YESTECH manual under Pradhan Mantri Fasal Bima Yojana (PFMBY) was followed. Latur district facing more weather-based yield losses during last few of years. In this case study we tried to estimate yield of soybean crop for agriculture-based stake holders, insurance companies, Government polices at Revenue circle level (RC). Multimodal approach is beneficial over single model yield estimation approach as it takes ensemble yield for perfect forecasting of crop yield. Accuracy was in the range as given in YESTECH manual at RC level. Thus, overall results show that use of such model for yield estimation is one of the best approach to take the decisions for insurance based stake holders in rainfed regions where more negative consequences on soybean productivity under different climate change scenario was observed.

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

IoT Based Crop Prediction using Machine Learning

To enhance agricultural productivity and resource allocation. Leveraging historical data encompassing various agricultural factors such as soil composition, weather conditions, and crop types, a predictive model is developed to forecast crop yields. Machine learning techniques, including regression and classification algorithms, are employed to analyze the intricate relationships within the dataset and predict the most likely outcomes for specific crops in different regions. The model is trained on a diverse dataset, considering variations in climate and soil characteristics, ensuring robustness and adaptability across different agricultural environments . The proposed crop prediction system not only provides accurate yield forecasts but also assists farmers in making informed decisions regarding crop selection and resource optimization. By harnessing the power of machine learning, this research contributes to sustainable agriculture by promoting precision farming practices. The integration of technology in crop prediction not only improves yield predictions but also supports the overall resilience of agricultural systems, fostering a data-driven approach that aligns with the evolving needs of the agricultural sector in a rapidly changing world.

Artificial Intelligence (AI)-driven Transformation: Sustainable Development of Agro-based Industries in Bihar

Digital technologies particularly Artificial Intelligence (AI), are needed to transform agricultural sector in Bihar. There is a vast opportunity in adopting AI-driven solutions to enhance productivity and sustainability as well as increase the profitability of agriculture in Bihar. The current status and potential applications of AI in agriculture will be reviewed, challenges and opportunities specific to regions identified, together with development of strategies for maximum gain from AI technologies. We used the bibliographic methods and SWOT analysis. The growth of Bihar’s GDP as the adoption of Internet of Thing (IoT), precision farming and AI is projected at 5-6% per year that may create up to 30% more job opportunities in agricultural sector by 2024.Precision agriculture combined with crop yield forecasting is among key applications of AI in agro industries, labor optimization and efficiency; soil monitoring and management; disease detection and pest management; weather forecasting for climate risk management purposes among others include weed identification herbicide optimization supply chain optimization. SWOT analysis reveals strength such as enhanced productivity, cost reduction, improved decision-making ability on farm operations and climate resilience improvement strategy while helps farmers accessing information at their fingertips. However, weaknesses include infrastructure limitations, skill gaps and data accessibility issues. Opportunities arise from government support, research and development initiatives and market expansion. Threats encompass the digital divide, privacy and data security concerns, and dependence on external factors. The integration of AI holds substantial relevance for Bihar offering a pathway to overcome existing agricultural challenges and unlock new growth opportunities.

Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators.

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017-2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production.

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

Harmonizing Cross View Image Transformation Through Local and Global Insights- A Review

Hyperspectral and multispectral information processing systems and technologies have demonstrated their value in enhancing agricultural productivity and practices by providing farmers and crop managers with valuable information on factors influencing crop condition and growth. These technologies play a crucial role in various agricultural applications, such as crop management, crop yield forecasting, crop disease detection, and monitoring soil, water, and land usage. To enhance the process of agriculture through effective crop management and assistance to farmer, using an advanced image transformation techniques the study delves into the exploration of techniques for harmonizing cross-view image transformation, with a focus on integrating both local and global insights. Further the study proposes the application of the 2D-DWT (Two-Dimensional Discrete Wavelet Transform) technique for image data preprocessing and the Extreme Learning Machine (ELM) algorithm for image classification in the context of hyperspectral images. Hyperspectral information sensing allows for the coverage of the electromagnetic spectrum in a single acquisition with several hundred spectral bands, resulting in a data cube containing significant spectral and spatial information ELM is particularly effective for classification problems because of its quick training time and capacity to operate with hidden nodes whose parameters are randomly assigned rather than modified iteratively. This comprehensive review aims to provide valuable insights and a critical analysis of the suggested method, shedding light on its potential contributions to the advancement of image transformation techniques for agricultural development.

Forecasting Agricultural Crop Profitability and Yield in India

Abstract: "India's heavy reliance on agriculture underscores the importance of accurately estimating agricultural production, considering the interplay of organic, economic, and seasonal variables, particularly amid a growing population. Predicting crop yields is crucial for farmers' planning, covering storage and marketing strategies. However, this task is complex and requires foresight. Data mining techniques emerge as a potent tool, leveraging extensive datasets to extract invaluable insights. By employing methods like Random Forest, this research offers a swift yet comprehensive examination of crop yield forecasts for specific regions. Predictive analyses like these act as a crucial asset, enabling stakeholders to make well-informed decisions grounded in expected patterns and trends.”

Soil Analyser - Revolutionizing Agriculture through Wireless Sensor Networks and Machine Learning

Abstract: Crop yield analysis, traditionally dependent on the experiential knowledge of farmers, has undergone a transformative revolution with the advent of machine learning. Anticipating yields stands as a critical concern for farmers eager for insights into their upcoming harvests. In the past, such predictions relied on a farmer's intimate familiarity with their specific field and crop. However, the challenge has persisted in effectively leveraging available data. Enter machine learning-a rapidly advancing field with compelling solutions. This research introduces a system that utilizes historical agricultural data to forecast crop yields. By employing sophisticated machine learning algorithms such as Support Vector Machine and Random Forest, this system not only predicts yields but also recommends optimal fertilizers for each crop. At its core, the emphasis is on constructing a robust predictive model that reliably anticipates future crop production. The paper meticulously delves into the realm of crop yield prediction, exploring the subject through the lens of advanced machine learning techniques.

Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI

The prediction of cereals yields today is very important for global food security and helps decision-makers in the import-export operations of countries, especially with the rise world population. The advent of remote sensing technologies in precision farming systems has made cereal yield predictions possible, providing valuable insights into the temporal and spatial variations in cereal conditions across both large and small-scale crop lands. Among the various vegetation indices used to analyze these conditions, the normalized difference of vegetation index (NDVI) has emerged as a key indicator. The main objective of this study is to evaluate the possibility of using MODIS-NDVI data to forecast the yield of cereal crops (wheat and barley) in semi-arid region of Algeria (Setif). Additionally, identify the optimal timing for reliable and accurate crop yield forecasts. The remote sensing data utilized in this study covered the growing seasons from February to June, from 2002 to 2022. The results indicated a strong correlation between cereal grain yield and NDVI from late February to mid-March, with R² values ranging from 0.55 to 0.82 for the two cereal species. The RMSE of the NDVI based prediction model ranged from 0.01 t ha-1 to 0.276 t ha-1. The approximate average increase in the grain yield of barley and wheat lies between 0.659 to 0.746 t ha-1 with an increase of 0.1 in NDVI value. These results demonstrate the effectiveness of using MODIS-NDVI data for cereal yield forecasting in semi-arid region of Algeria, offering valuable predictions two to three months before the harvest.