Digital Assist to Indian Farming
Keywords:
Precision farming, Smart agriculture, Crop yield prediction, Market demand–supply Forecasting, IoT in agriculture, Machine Learning, Voting classifier, Ensemble learning, Crop recommendation system, Yield estimation, Data-Driven farming, Agricultural analytics, Threshold-based prediction, Smart farming dashboard, AI-Based decision support systemAbstract
The practice of cultivating the soil, producing crops, and keeping livestock is referred to as farming. Agriculture is critical to a country’s economic development, with nearly 58 per cent of the population depending on it as their primary source of livelihood. Traditionally, Indian farmers have adopted conventional farming techniques, often without awareness of market demand and supply trends. As a result, they face reduced productivity, time-consuming practices, and uncertainty in receiving fair prices for their crops. Precision farming offers a solution by integrating advanced technologies such as IoT, Data Analytics, and Machine Learning to analyze soil conditions, predict weather, recommend suitable crops, and estimate crop yield. In addition to these capabilities, the proposed work also focuses on analyzing historical demand–supply data of agricultural produce. By predicting the expected yield and mapping it against market demand, the system can recommend not only which crops to grow but also the optimal percentage share of each crop, along with the probability of achieving profitable outcomes. This dual approach—combining precision farming with market demand–supply forecasting—helps farmers pre-plan their cultivation activities, optimize resource use, and increase productivity while ensuring better alignment with commercial market needs. Ultimately, this integrated framework enables farmers to achieve higher yields at lower costs and secure expected prices for their produce, thereby promoting sustainable and smart farming practices. This study analyzes crop production trends in India over the last decade. Using government data, we examine the supply- demand gap in rice, wheat, and maize. Our findings reveal an increasing demand-supply imbalance due to population growth, highlighting the need for sustainable agricultural policies.
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