Predict Crop Production Research Articles

A legal approach: artificial intelligence, agribusiness and the 2030 agenda

Artificial intelligence (AI) is revolutionizing the agribusiness sector, offering significant potential to improve efficiency, sustainability and productivity. By analyzing large amounts of data, AI can optimize resource management, predict crop productivity and detect diseases early, leading to increased agricultural production and reduced environmental impact. However, integrating AI into agriculture faces several challenges, including technological infrastructure, data accessibility, and regulatory hurdles. To fully realize the benefits of AI, it is crucial to address these challenges and develop a robust legal framework that protects intellectual property, ensures data privacy and encourages innovation. By aligning AI-driven agricultural practices with the UN Sustainable Development Goals, we can create a more sustainable and equitable future for global food production.

Model for estimating red mulberry leaf area using a genetic algorithm

The success of sericulture depends directly on the quality and quantity of mulberry leaves, as it is essential for the feeding and development of silkworm caterpillars and, consequently, influences the quality of the silk thread manufactured. The estimation of mulberry leaf area is important to have plant development and growth indicators, such as transpiration intensity, net assimilation rate, leaf area ratio, specific leaf area and leaf area index, which allow predicting crop productivity. Thus, the objective of this study was to develop and test a model capable of estimating the red mulberry leaf area using a genetic algorithm. The model was adjusted with the proposed stochastic optimization method. The mean error found for the tested dataset was approximately 228.17 mm2 in sample space with mean leaf area of 6515.55 mm2. The information generated allows applying the model to estimate red mulberry leaf area in future studies.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LSTnighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

Influence of mulches on soil moisture and water infiltration in the tomato crop

Soil moisture is a key parameter of soil monitoring for observation of vegetation growth, predicting crop production, and improving water resource management. In this study, the objective is to compare the evolution of soil moisture in different mulches to determine the best mulch and its characteristics of infiltration in the soil. The experiment was conducted during the summer season in July-September 2022 on four different mulches (wood chips, sawdust, straw, mixture), and control at the experimental plot of Blida. The results showed that silt is the main matrix of the soil. The analysis of infiltration data identified modified Kostiakov as the best model of the study site, whose period of plant growth represents the phase during which we have a better infiltration under the mixture. The application of the mulch changes the moisture mainly at 15 cm and the conductivity at 10 cm. In addition, the mixture is the best mulch to conserve moisture in the soil while reducing the frequency of irrigation. The correlation between soil moisture and conductivity was overall very good. This was due to the effect of mulch, soil texture, plant root development, and capillary rise.

Crop models and their use in assessing crop production and food security: A review

AbstractAgriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security.

Survival and Comparative study on Different Artificial Intelligence Techniques for Crop Yield Prediction

Agriculture is an essential, important sector in the wide-reaching context. Farming helps to satisfy the basic need of food for every living being. Agriculture is considered the broadest economic sector. The crop yield is a significant part of food security and improves the drastic manner by human population. The quality and quantity of the yield touch the high rate of production. Farmers require timely advice to predict crop productivity. The strategic analysis also helps to increase crop production to meet the growing food demand. The forecasting of crop yield is a process of forecasting crop yield by using historical data. Machine learning provides a revolution in the agricultural field by changing the income scenario and growing an optimum crop. Many researchers carried out their research to deal with forecasting crop yield. In this way, accurate prediction of crop yield was improved. But, failed to reduce the crop yield prediction time and the accuracy level was not enhanced by existing methods.

Estimation of Productivity and Above-Ground Biomass for Corn (Zea mays) via Vegetation Indices in Madeira Island

The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this study was to estimate both productivity and above-ground biomass (AGB) for the corn crop by applying different vegetation indices (VIs) via high-resolution aerial imagery. Among the indices tested, strong correlations were obtained between productivity and the normalized difference vegetation index (NDVI) with a significance level of p < 0.05 (0.719), as well as for the normalized difference red edge (NDRE), or green normalized difference vegetation index (GNDVI) with crop productivity (p < 0.01), respectively 0.809 and 0.859. The AGB results align with those obtained previously; GNDVI and NDRE showed high correlations, but now with a significance level of p < 0.05 (0.758 and 0.695). Both GNDVI and NDRE indices showed coefficients of determination for productivity and AGB estimation with 0.738 and 0.654, and 0.701 and 0.632, respectively. The use of the GNDVI and NDRE indices shows excellent results for estimating productivity as well as AGB for the corn crop, both at the spatial and numerical levels. The possibility of predicting crop productivity is an essential tool for producers, since it allows them to make timely decisions to correct any deficit present in their agricultural plots, and further contributes to AI integration for drone digital optimization.

RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background

The green leaf area index (LAI) is an important structural parameter that can be used for monitoring plant transpiration and carbon cycling over large spatial extents. LAI is also critical for understanding biophysical processes and predicting crop productivity. However, the retrieval of wheat green LAI based on canopy reflectance is always affected by the background material. For example, in the Yangtze River Basin of China, rice residues are typically returned to rice–wheat rotation fields. Thus the background for observing wheat canopy varies from a mixture of traditional wheat-soil components to wheat-residue or wheat-residue-soil, posing a significant challenge for accurate LAI estimation at the early stages of crop growth. Furthermore, when current vegetation indices (VIs) are used to estimate LAI, leaf chlorophyll content (LCC) and field moisture are two additional confounding factors. To resolve these issues, we propose a novel residue-soil adjusted red edge difference index (RSARE), which draws on concepts from a simple physical algorithm, i.e., linear spectral mixture analysis (LSMA). The results of field, satellite and modeling experiments demonstrate that the RSARE-LAI model is generally not sensitive to LCC, or to variations in residue-soil and traditional soil backgrounds, and estimates LAI with high accuracy (RMSEval = 0.55, RRMSEval=20.71%). In comparison to traditional VI-LAI maps, RASRE-LAI maps are less sensitive to confounding factors associated with the background components, field moisture, LCC, and both inter-annual and geographical differences. This novel residue-soil background-resistant RSARE particularly improves the accuracy of wheat LAI estimation at low LAI levels, thus facilitating large-scale mapping of LAI in the early growing season.

Estimating energy consumption and GHG emissions in crop production: A machine learning approach

It is necessary to observe the relationship between the energy inputs and outputs of agricultural production in the context of long-term strategies to determine optimal sustainable solutions and identify the most susceptible system components. Comprehensive research to assess energy performance data over a relatively long-term is required to predict crop production and greenhouse gas (GHG) emissions based on energy inputs. This study collects and analyzes energy inputs and GHG emissions from 17 crops of Iran in five major categories (cereal, pulse, oilseed, fiber, and tubers) during 1970–2019 to quantify the long-term energy efficiency of major crops. Three machine learning (ML) algorithms are developed to quantify the relationship between the resources consumed per unit of energy output and GHG emission models. These models are evaluated through three quantitative statistics. The analysis considers all physical and chemical inputs used to produce the major crops. These energy variables include human labor, machinery, fossil fuels, electricity, irrigation water, fertilizers (phosphate, nitrogen, and potassium), pesticides and seed rate. The results revealed that the input and output energy values increased from 39.47 to 190.18 MJ ha-1 in 1970 to 253.43 and 654 MJ ha−1 in 2019. The GHG emissions from electricity (47.1%) were followed by those of nitrogen fertilizer (25.75%). Reducing the consumption of electricity and nitrogen fertilizers is the most efficient options to improve the energy efficiency of the crops studied.

Predicting Crop Yield Using Deep Learning and Remote Sensing

The art of predicting crop production is done before the crop is harvested. Crop output forecasts will help people make timely judgments concerning food policy, prices in markets, import and export laws, and acceptable warehousing. It is possible to reduce the socioeconomic effects of crop loss brought on by a natural disaster, such as a flood or a drought, and to organize humanitarian food assistance. It has been suggested that deep learning, which lets the model to automatically extricate features and learn from the datasets, could be useful for predicting agricultural yields. This review helps to understand that how vegetation indices and environmental variables affect agricultural output by revealing gaps in our understanding of deep learning methodologies and remote sensing data in a specific area. Literature review of 2011-2022 has been collected from different databases and sites and analyzed to meet the aims of this review. The study mainly focused on the benefits of machine learning, and remote sensing for forecasting crop yield. The most often employed form of remote sensing is satellite technology, namely the usage of the Moderate-Resolution Imaging Spectro radiometer. Vegetation indices referred to as the most often employed attribute for forecasting crop yield, according to the results. This review compares all these techniques and pros and cons related to them.

Machine Learning Based Crop Prediction on Region Wise Weather Data

Agriculture is a primordial occupation for human civilization, whereby farmers cultivate domesticated species of food. It refers to farming in general, which is an art and science that attempts to reform a component of the Earth's exterior through the cultivation of plants and other crops, as well as raising livestock for sustenance or other necessities for the soul and economic gain. As a result of the vital role that sustainable agriculture plays in the overall health of the nation, this sector of the economy has been the incubator for some of the most cutting-edge technological advances in recent history. Scientists and farmers have been working together to discover new methods that will allow them to increase crop production while simultaneously decreasing their water consumption and lessening their negative effects on the environment. Machine learning, deep learning, and a number of other methodologies are some examples of these approaches. A crop's expansion and maturation are both heavily influenced by the climate in which it is grown. The local climate, namely its wind speed, temperature, rainfall, and humidity, is the most exigent factor in determining the advancement or failure of crop production. If the weather is predicted prior to crop cultivation, it will be beneficial to the farmer. Machine learning is a new innovation that can solve people’s real-life problems. It is a technique where a machine can act like a human and learn through experiences and the use of different types of data. Now a day, Agriculture is one of the fields of machine learning where we use different types of machine learning algorithms to predict crop production based on climate data which can benefited farmers to increase the production of the crop. In these studies, we are going to predict crop yield using LSTM based on predicted weather data.

Effects of salinity on soil properties of coastal areas of Bagerhat and Pirojpur districts

A study was conducted to observe the soil properties under naturally occurring saline soil conditions in Bagerhat and Pirojpur. Soil samples at 0-20 cm depth were collected from Bagerhat sadar and Khachua upazila under Bagerhat district and Projpur sadar and Nazirpur upazila under Projpur district. Observations were made on soil pH, organic matter, cation exchange capacity (CEC), electrical conductivity (EC), exchangeable sodium percentage (ESP), total N, available P, S and exchangeable K, Ca, Na and Mg contents. Result indicates that pH value ranged from 6.70 to 7.40 and 6.60 to 7.79, organic matter 1.75 to 3.61% and 1.57 to 2.90 %, total N 0.11 to 0.18% and 0.08 to 0.97 %, available P 7.40 to 39.18 mg/kg and 2.92 to 23.40 mg/kg and 0.20 to 1.04%, available S 15.53 to 66.82 mg/kg and 3.50 to 35.53 mg/kg in Bagerhat and Pirojpur districts, respectively. In Bagerhat and Pirojpur districts, EC varied from 2.84 to 7.10 ds/m and 1.16 to 4.90 ds/m, CEC 9.77 to 36.35 and 10.34 to 48.70 meq100g-1 soils, exchangeable Na 0.62 to 2.80 meq100g-1 soil and 0.75 to 2.11 meq100g-1 soil, exchangeable K 4.20 to 22.68 meq100g-1soil and 3.64 to 41.04 meq100g-1 soil, exchangeable Ca 0.84 to 8.37 meq100g-1 and 0.80 to 17.82 meq100g-1, exchangeable Mg 0.13 to 0.60 meq100g-1 soil 0.17 to 0.38 meq100g-1 soil and ESP 4.36 to 11.72 and 3.12 to 7.34, respectively. In Bagerhat, a positive significant correlation of CEC was found with total N, K, EC, Na, K, Ca and Mg contents. In Pirojpur, a negative significant correlation of CEC was found with OM, S and Na contents. The paired shows that S, EC, CEC, K, Ca and ESP will significant between the two locations. These results would be useful for predicting crop production and varietal response to soil nutrient conditions and developing fertilizer management in the coastal areas of Bangladesh. Bangladesh J. Agri. 2022, 47(2): 143-160

From Laboratory to Field: Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild.

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization, namely, Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training. Specifically, MSUN comprises multirepresentation, subdomain adaptation modules and auxiliary uncertainty regularization. The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain. This effectively alleviates the problem of large interdomain discrepancy. Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation. Finally, the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer. MSUN was experimentally validated to achieve optimal results on the PlantDoc, Plant-Pathology, Corn-Leaf-Diseases, and Tomato-Leaf-Diseases datasets, with accuracies of 56.06%, 72.31%, 96.78%, and 50.58%, respectively, surpassing other state-of-the-art domain adaptation techniques considerably.

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.

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis.

RETRACTED: Monitoring system of agricultural performance based on multisensor data and cross‐validation technique in merging temporal images

AbstractSeveral approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high‐quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables

Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R2 = 0.81–0.83, RMSE = 0.40–0.41 kg/m2) than the models based on single SAR polarization indices or their combinations, or optimized features (R2 = 0.04–0.39, RMSE = 0.84–1.08 kg/m2). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R2 = 0.83 and RMSE = 0.39 kg/m2, much better than single VIs or their combination, or optimized features (R2 of 0.31–0.77, RMSE of 0.47–0.87 kg/m2). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R2 = 0.85, RMSE = 0.38 kg/m2) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling.

농업 공공 빅데이터를 이용한 머신러닝 기반 생산량 및 판매 수익금 예측

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.