A Process for Improved Agriculture: Harvest Forecasting

Kumar, Prafulla; Verma, Pooja

doi:10.1109/smart55829.2022.10047435

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2024

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“…The paper [14] focuses on the development of a process for spray forecasting in agriculture. It proposes a methodology that utilizes data analysis techniques to predict crop yields and improve decision-making for farmers.…”

Section: Related Workmentioning

confidence: 99%

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

Fondaj,

Hamit,

Krrabaj

et al. 2024

IJITS JOURNAL

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The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

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Section: Related Workmentioning

confidence: 99%