Deep Learning; A Climate Smart Agriculture Tool for Groundnut Farmers

Groundnut is an oil seed crop, which is grown widely in the country and approximately 80% of groundnut is produced in the rainfed condition.Unlike weather factors even the prices of agriculture commodities are volatile in nature and the groundnut prices also behaves in an unusual pattern.A traditional farmer faces recurrent challenges.Deep learning methods and the accessibility of satellite imagery have, however, created new opportunities for more accurate and effective agricultural yield estimates.Large-scale yield estimation and understanding the impact of the variability of agricultural growing circumstances are critical due to the increased frequency of extreme climate occurrences.Crop growth condition models can be utilized with time series of spatially explicit information from satellite remote sensing (RS).For efficient agricultural management, guaranteeing food security and making wise decisions about resource allocation and market forecasting, accurate and timely estimation of crop yields is essential.Crop yield estimation has historically depended on time-consuming field surveys and statistical models.Machine Learning (ML) is an exciting application of Artificial Intelligence.It provides the ability to learn by experiences without any explicit.The proposed model is based on simple\and cost-effective hardware that can be used by agriculture officers and farmers to get good productivity of crops.SCS model is trained by classifying dataset and tested subsequently.The accuracy and performance of an ML classifier depend only on the type and size of the dataset.Crop selection by real-time sensing data and soil analysis attributes is a big contribution in research of smart agriculture.A model was proposed basing on three modules: crop selection, crop management and crop maturity.It used parameters soil moisture, temperature, humidity, air pressure and air quality with weather conditions for better crop selection and health monitoring.A real-time sensory data was used for analysis on Thing Speak application with KNN algorithm.Some data mining techniques are applied for data preprocessing and comparing real-time data with trained data for crop prediction.It also considered crop prices for crop prediction, listed on National Commodity and Derivative Exchange.The KNN classifier is applied for data analysis has focused on IoT-oriented agricultural methods for weather monitoring.The prediction methods are investigated for commercial and scientific perspectives, cost of IoT components, Think Speak application is used for data analysis.An Android application is also designed to intimate the farmers about required water level of fields.IoT framework best use of land to improve farming methods and increase crop production with profit maximization.A wireless sensor network was deployed in the field to sense data for different parameters and for proper monitoring of field.It proposed a crop prediction method for crop yield maximization and quality of crops by considering real-time data of metrological factors using ML algorithms: precipitation, temperature, humidity and solar light.Soft computing techniques can be employed to estimate the yield of various crops.As a result of rapid advancements in technology, crop models and decision tools have emerged as vital components of precision agriculture worldwide.These models and tools utilize linear regression techniques, non-linear simulations, expert systems,