دورية أكاديمية

Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique

التفاصيل البيبلوغرافية
العنوان: Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique
المؤلفون: Yashashree Mahale, Nida Khan, Kunal Kulkarni, Shivali Amit Wagle, Preksha Pareek, Ketan Kotecha, Tanupriya Choudhury, Ashutosh Sharma
المصدر: Discover Sustainability, Vol 5, Iss 1, Pp 1-23 (2024)
بيانات النشر: Springer, 2024.
سنة النشر: 2024
المجموعة: LCC:Environmental sciences
مصطلحات موضوعية: Crop recommendation, Expectation-maximization, LSTM, Weather forecast, Environmental sciences, GE1-350
الوصف: Abstract Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra’s agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers’ decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2662-9984
العلاقة: https://doaj.org/toc/2662-9984Test
DOI: 10.1007/s43621-024-00292-5
الوصول الحر: https://doaj.org/article/308ca706c1b64746a6efbc157feb4abaTest
رقم الانضمام: edsdoj.308ca706c1b64746a6efbc157feb4aba
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:26629984
DOI:10.1007/s43621-024-00292-5