Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

Ahmed, Moiz Uddin; Hussain, İqbal

doi:10.1016/j.telpol.2022.102370

Cited by 29 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2022), livestock (Goel et al. , 2022a), also they aid to forecast production such as in the case of Pakistani wheat (Ahmed and Hussain, 2022), to solve crop selection problems (Kumar et al. , 2015) and to boost crop yield (Fenu and Malloci, 2021; Jain and Choudhary, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, AI plays a key role in enhancing the adoption of sustainable practices in agriculture practices to meet sustainable development goals (SDGs) (Abban and Abebe, 2022;Morales and Elkader, 2020). (Anand et al, 2022;Khan et al, 2022), livestock (Goel et al, 2022a), also they aid to forecast production such as in the case of Pakistani wheat (Ahmed and Hussain, 2022), to solve crop selection problems (Kumar et al, 2015) and to boost crop yield (Fenu and Malloci, 2021;Jain and Choudhary, 2022). However, sustainability and agriculture production efficiency are intertwined, as AI could enhance sustainable digital transformation (Lugonja et al, 2022) and farming practices (Alam et al, 2020).…”

Section: Bibliographic Coupling Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

View full text Add to dashboard Cite

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

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Bibliographic Coupling Analysismentioning

confidence: 99%

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

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

View full text Add to dashboard Cite

show abstract

“…Supervised ML algorithms, such as RF and SVM, were employed by Dubois [ 30 ] to develop models for predicting soil water potential in potato farming. Likewise ML algorithms were used by Ahmed and Hossain [ 31 ] to forecast wheat yield and multilayer perceptron (MLP), decision table, JRip algorithms (IoT framework) were employed by Gutiérrez [ 32 ] to suggest crop strategies techniques. Rajak [ 33 ] also used a soil database collected from farms and a dataset from a soil testing laboratory to recommend a crop based on site-specific parameters using SVM and ANN as machine learning models.…”

Section: Introductionmentioning

confidence: 99%

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

Dey,

Ferdous,

Ahmed

2024

Heliyon

View full text Add to dashboard Cite

“…To address these challenges, researchers increasingly turn to nonlinear models for agricultural yield estimation [8,9]. The application of ML for estimating agricultural yields has gained significant attention in recent years [10,11]. Various ML techniques have been extensively utilized to achieve precise predictions for the yields of diverse crops.…”

Section: Introductionmentioning

confidence: 99%

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

Ashfaq,

Khan,

Alzahrani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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

show abstract

Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

Cited by 29 publications

References 51 publications

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

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

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

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

Contact Info

Product

Resources

About