Prediction of crop yield using machine learning

Shaik, Mohammed Ali; Manoharan, Geetha; Prashanth, B.; Akhil, Nune; Akash, Anumandla; Reddy, Thudi Raja Shekhar

doi:10.1063/5.0081726

Cited by 37 publications

(2 citation statements)

References 30 publications

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“…Incidence of pests and theft was ranked the fourth major challenge. This is also reported from the findings of Shaik et al [34] on crop yield prediction using machine learning. Inadequate market information, unfavorable climatic condition and poor supply of vegetable seeds was ranked the fifth, sixth and seventh challenge respectively.…”

Section: Constraints To Vegetable Production In the Study Areasupporting

confidence: 72%

Unlocking the Economic Potentials of Leafy Vegetable: A Case Study of Farmers in Akoko South West Local Government Area of Ondo State, Nigeria

Oladoyin,

Adegoroye,

Akinbola

et al. 2023

AJRAF

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This study analysed the economic potentials of leafy vegetable among farmers in Akoko South West Local Government Area of Ondo State, Nigeria. Primary data were used for this study with a well- structured questionnaire and personal interview for the collection of the data with a sample size of 150 farmers. The data were analyzed using descriptive statistics, budgetary technique and multiple regression analysis. The analysis of the data showed that about two-third of the farmers were male,74.7% married and the majority (58.7%) of the respondents were in their economic active age (41-50 years). Majority (94%) of the farmers were educated with the average farming experience of 14 years. The results also revealed that the major types of vegetables available in the study area were; Africa spinach, Pumpkin, water leaf, jute leaf and bitter leaf. The result on return on investment was 2.31, implying that for every N1 invested on vegetable production, there is a return of N2.31kobo which is an indication that the business is worthwhile. The study also revealed the gender, age of farmers, marital status, level of education and household size were the factors that significantly and positively influence leafy vegetable output in the area. The most serious problems encountered by the vegetable farmers were; inadequate capital, unstable price of vegetables, the incidence of pest, pilfering and theft and inadequate information. Thus, it is recommended that agricultural credit should be provided for vegetable farmers to boost vegetable production.

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Section: Constraints To Vegetable Production In the Study Areasupporting

confidence: 72%

Unlocking the Economic Potentials of Leafy Vegetable: A Case Study of Farmers in Akoko South West Local Government Area of Ondo State, Nigeria

Oladoyin,

Adegoroye,

Akinbola

et al. 2023

AJRAF

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“…The paper predicts crop yield using Arti cial Neural Network, considering factors such as soil type, environmental conditions, and crop selection. Authors express gratitude for support while acknowledging the decline of agriculture's contribution to India's economy due to various factors [14].…”

Section: The Study Compared Extreme Learning Machine (Elm) With Multi...mentioning

confidence: 99%

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Asif,

Wahid

2024

Preprint

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This study investigates how machine learning (ML) algorithms can be used in agriculture to forecast soil fertility and maximize crop yield. Machine learning (ML) models are created to predict soil nutrient levels, pH, and organic matter content across a range of geographical locations and land-use types with high accuracy by evaluating large datasets that include soil samples, environmental conditions, and agronomic methods. The research shows the advantages of nonlinear modeling approaches in capturing complex interactions inherent in agricultural systems through a comprehensive evaluation of several machine learning techniques, including ensemble methods like AdaBoost and Extra Tree Classifier. Furthermore, immediate insights and recommendations for improving agronomic decisions are made possible by the integration of real-time sensing technologies, such as proximate sensing, distant sensing, and Internet of Things (IoT) devices. Overall, this work highlights how machine learning (ML) can completely change crop management techniques and soil fertility prediction, enabling a more resilient and sustainable agriculture sector.

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A Machine Learning-Driven Soil Nutrient and Crop Yield Recommendation Platform with Pesticide Suggestions

Malathy,

Shilpa,

Abhinaya

et al. 2024

Lecture Notes in Networks and Systems

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Prediction of crop yield using machine learning

Cited by 37 publications

References 30 publications

Unlocking the Economic Potentials of Leafy Vegetable: A Case Study of Farmers in Akoko South West Local Government Area of Ondo State, Nigeria

Unlocking the Economic Potentials of Leafy Vegetable: A Case Study of Farmers in Akoko South West Local Government Area of Ondo State, Nigeria

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

A Machine Learning-Driven Soil Nutrient and Crop Yield Recommendation Platform with Pesticide Suggestions

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