Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.

Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models

Abstract: Strawberry is a high value and labor-intensive specialty crop in California. The three major fruit production areas on the Central Coast complement each other in producing fruits almost throughout the year. Forecasting strawberry yield with some lead time can help growers plan for required and often limited human resources and aid in making strategic business decisions. The objectives of this paper were to investigate the correlation among various weather parameters related with strawberry yield at the field level and to evaluate yield forecasts using the predictive principal component regression (PPCR) and two machine-learning techniques: (a) a single layer neural network (NN) and (b) generic random forest (RF). The meteorological parameters were a combination of the sensor data measured in the strawberry field, meteorological data obtained from the nearest weather station, and calculated agroclimatic indices such as chill hours. The correlation analysis showed that all of the parameters were significantly correlated with strawberry yield and provided the potential to develop weekly yield forecast models. In general, the machine learning technique showed better skills in predicting strawberry yields when compared to the principal component regression. More specifically, the NN provided the most skills in forecasting strawberry yield. While observations of one growing season are capable of forecasting crop yield with reasonable skills, more efforts are needed to validate this approach in various fields in the region.

SMS-based Smarter Agriculture decision support system for yellow corn farmers in Isabela

Abstract: Agriculture is a major industry in the Philippines with 50% of its work force working in agriculture. Isabela is the number one corn producer in the country, contributing 22.57% of the national production, housing 20.26% of the national area producing corn. Farmers in this area use traditions and superstitions in making farming decisions. While this worked in the past, the recent progression of climate change has prompted the need for technology to provide advice to farmers so that they may take into consideration the big factors that must be considered when farming namely the atmosphere, cultivar, soil and farm management. This paper presents a Smarter Agriculture solution through precision agriculture. This solution leverages on DSSAT 4.5 with Automated Weather Station (AWS) sensors, numerical climate model, numerical weather model, corn cultivar parameters, SMS technology as well as the expert knowledge of the farmers. The solution is designed for yellow corn farmers in Isabela where farmers have limited Internet connection and have difficulty in using smart phones hence, the use of SMS. This paper will focus on how the solution is developed so that farmers may be able to identify the best date to start planting, best date to harvest, optimal water as well as the projected dry yield for the crop, taking into consideration the climate outlook during the planting season. This allows the farmer to query about the weather outlook for short-term farming decisions such as decisions on whether to apply fertilizer or irrigation during the day.