Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Heliyon, Journal Year: 2024, Volume and Issue: 10(3), P. e25112 - e25112

Published: Jan. 26, 2024

Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations suggest appropriate crops or supplementary nutrients achieve the highest possible production. The aim this study was evaluate efficacy five distinct ML models for a dataset sourced from Kaggle repository generate practical recommendations selection determination required nutrient(s) in given site. datasets contain information on NPK, pH, three variables: temperature, rainfall, humidity. namely Support vector machine, XGBoost, Random forest, KNN, Decision Tree were trained using yields individual sets 11 10 horticultural crops, as well combined both agri-horticultural crops. results strongly separately each category rather than categories better predictions. Comparing models, XGBoost demonstrated level accuracy. precision rates recommending combination 99.09 % (AUC 1.0), 99.3 98.51 0.99), respectively. This non-intrusive method generating diverse environmental conditions holds potential provide valuable insights development user-friendly AI cloud-based interface. Such an interface would enable rapid decision-making optimal fertilizer applications suitable cultivation at specific sites.

Language: Английский

---

Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

European Journal of Agronomy, Journal Year: 2023, Volume and Issue: 147, P. 126837 - 126837

Published: April 18, 2023

Language: Английский

---

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 40947 - 40961

Published: Jan. 1, 2024

Due to exponential population growth, climate change, and an increasing demand for food, there is unprecedented need a timely, precise, dependable assessment of crop yield on large scale. Wheat, staple worldwide, requires accurate prompt prediction its output global food security. Traditionally, the development empirical models forecasting has relied data, satellite or combination both. Despite enhanced performance achieved by integrating contributions from various sources (Climate, Soil, Socioeconomic, Remote sensing) remain unclear. The lack well-defined comparisons between regression-based approaches different Machine Learning (ML) methods in necessitates further investigation. This study addresses gaps combining data multiple forecast wheat Multan region Punjab province Pakistan. findings are compared benchmark provided Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), Least Absolute Shrinkage Selection Operator (LASSO)) publicly available within GEE (Google Earth Engine) platform, including climate, satellite, soil properties, spatial information develop alternative using 2017 2022, selecting best attribute subset related output. set district-level simulated yields was analyzed Machin (SVM, RF, LASSO) as function seasonal weather, soil. results indicate that all datasets algorithms achieves better ( R ² : 0.74-0.88). Incorporating other properties into can improve 0.08 0.12. forest outperformed competitor Root Mean Square Error (RMSE) 0.05 q/ha 0.88. Comparative analysis shows random 97% SVM 93% yielded area.

Language: Английский

---

An automatic ensemble machine learning for wheat yield prediction in Africa

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(25), P. 66433 - 66459

Published: Jan. 23, 2024

Language: Английский

---

Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(14), P. 2566 - 2566

Published: July 12, 2024

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such soil properties, climate, relief, hydrology and socio-economic aspects. The aim this study was to evaluate soils for wheat cultivation Gavshan region, Iran, country is facing task becoming self-sufficient wheat. Various methods were used land, multi-criteria decision-making (MCDM), which proving be land use planning. MCDM machine learning (ML) are useful processes because they complicated spatial data that widely available. Using a geomorphological map, seventy profiles selected described, ten properties yields determined. Three approaches, including technique preference ordering by similarity ideal solution (TOPSIS), gray relational analysis (GRA), simple additive weighting (SAW), evaluated. criteria weights extracted using Shannon’s entropy method. Random forest (RF) model auxiliary variables (remote sensing data, terrain maps) represent values. Spatial autocorrelation statistical method applied analyze variability data. Slope, CEC (cation exchange capacity), OC (organic carbon) most factors cultivation. between key (slope, CEC, OC) yield confirmed these results. These results also showed significant correlation values TOPSIS, GRA, SAW (0.74, 0.72, 0.57, respectively). distribution areas classified good according TOPSIS GRA larger than those moderate weak approach. techniques with yield. In addition, RF its effectiveness processing complex improved accuracy assessment. study, integrating advanced ML, applicable approach proposed, can improve considering sustainability principles management.

Language: Английский

---

Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 6976 - 6976

Published: Aug. 14, 2024

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact changing climatic conditions on crop yield, particularly staple crops like wheat, raised concerns about future production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide insights into how may affect wheat output. This research uses advanced machine learning models explore intricate relationship between prediction. Machine used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, several types ANNs: ANN (multi-layer perceptron), (probabilistic neural network), (generalized feed-forward), (linear regression). model was evaluated validated against yield weather data from three Punjab, Pakistan, regions (1991–2021). calibrated response downscaled (GCM) outputs SRA2, B1, A1B average collective CO2 emissions scenarios anticipate changes through 2052. Results showed that maximum temperature (R = 0.116) primary factor affecting preceding Tmin 0.114), while rainfall had a negligible 0.000). 0.988, nRMSE= 8.0%, MAE 0.090) demonstrated outstanding performance, outperforming Random Forest Regression 0.909, nRMSE 18%, 0.182), ANN(MLP) 0.902, 0.238, 17.0%), boosting tree 20%, 0.198). ANN(PNN) performed inadequately. RF better results R2 0.953, 0.791. expected is 5.5% lower than greatest reported at site study predicts site-specific output will experience loss due change. decrease, which anticipated be highest ever recorded, points might worsen insecurity. Additionally, findings highlighted approaches leveraging strengths could offer more accurate reliable predictions under varying scenarios. suggests developing climate-resilient agricultural practices, paving way sustainable security solutions.

Language: Английский

---

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

British Food Journal, Journal Year: 2023, Volume and Issue: 125(13), P. 482 - 515

Published: Aug. 16, 2023

Purpose This study analyses the literature on artificial intelligence (AI) and its implications for agri-food sector. research aims to identify current streams, main methodologies used, findings results delivered, gaps future directions. Design/methodology/approach relies 69 published contributions in field of AI It begins with a bibliographic coupling map streams proceeds systematic review examine topics contributions. Findings Six clusters were identified: (1) adoption benefits, (2) efficiency productivity, (3) logistics supply chain management, (4) supporting decision making process firms consumers, (5) risk mitigation (6) marketing aspects. Then, authors propose an interpretive framework composed three dimensions: two sides AI: “hard” side concerns technology development application while “soft” regards stakeholders' acceptance latter; level analysis: firm inter-firm; impact value activities Originality/value provides insights into extant sector, paving way inspiring practitioners different approaches traditionally low-tech

Language: Английский

---

Emerging Trends in Wheat (Triticum spp.) Breeding: Implications for the Future

Frontiers in Bioscience-Elite, Journal Year: 2024, Volume and Issue: 16(1), P. 2 - 2

Published: Jan. 31, 2024

Wheat (Triticum spp and, particularly, T. aestivum L.) is an essential cereal with increased human and animal nutritional demand. Therefore, there a need to enhance wheat yield genetic gain using modern breeding technologies alongside proven methods achieve the necessary increases in productivity. These will allow breeders develop improved cultivars more quickly efficiently. This review aims highlight emerging technological trends used worldwide breeding, focus on enhancing yield. The key for introducing variation (hybridization among species, synthetic wheat, hybridization; genetically modified wheat; transgenic gene-edited), inbreeding (double haploid (DH) speed (SB)), selection evaluation (marker-assisted (MAS), genomic (GS), machine learning (ML)) hybrid are discussed current opportunities development of future cultivars.

Language: Английский

---

Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms

Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, Journal Year: 2025, Volume and Issue: 28(1), P. 247 - 255

Published: Jan. 30, 2025

The rapid increase in the global population and evolving dietary habits have significantly heightened demand for high-quality protein sources. Beef, as a vital source, plays crucial role meeting this growing demand. This study aims to develop evaluate machine-learning model predict beef production using meteorological, agricultural, economic data. To achieve this, three different machine learning algorithms—Linear Regression, Random Forest, k-Nearest Neighbors—were employed. results indicate that Forest algorithm outperformed other methods terms of R² error metrics, demonstrating superior predictive accuracy. highlights potential techniques predicting production, offering valuable insights stakeholders involved strategic decision-making meet nutritional needs. As continues rise, importance such models becomes increasingly significant, emphasizing distinct advantages approaches provide context.

Language: Английский

---

Smart Farming and Precision Agriculture and Its Need in Today’s World

Signals and communication technology, Journal Year: 2024, Volume and Issue: unknown, P. 19 - 44

Published: Jan. 1, 2024

Language: Английский

---