• P.S.S. Gopi and Dr. M. Karthikeyan

Crop yield prediction is one of the major problems encountered in precision agriculture, and several techniques were modelled and authenticated till now. This problem demands the utility of numerous data as crop yield relies upon distinct elements namely seed variety, climate, weather, use of fertilizer, and soil. Machine learning (ML) is a significant decision support tool for crop yield prediction, which includes taking decisions regarding the crop types that will be suitable for a particular growing season. Numerous ML techniques were enforced to support the study on crop yield prediction. This article introduces an Artificial Bee Colony with Deep Belief Network based Crop Yield Estimation (ABCDBN-CYE) for Precision Agriculture. The intention of the ABCDBN-CYE technique is the estimation of crop productivity in the agricultural sector. The presented ABCDBN-CYE technique operates in two stages such as yield estimation and parameter tuning. At the preliminary stage, the ABCDBN-CYE technique makes use of DBN model to estimate the crop yield. Next, in the second stage, the ABC algorithm is exploited as a hyperparameter tuning approach to improve the estimation efficiency of the DBN model. To demonstrate the improved prediction performance of the ABCDBN-CYE technique, a widespread experimental analysis is performed. The comparison study showed the enhancements of the ABCDBN-CYE technique over other ones.

Precision agriculture; Prediction model; Crop yield; Deep learning; Metaheuristics

"Artificial Bee Colony with Deep Belief Network based Crop Yield Estimation for Precision Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.b624-b633, October-2022, Available :http://www.jetir.org/papers/JETIR2210188.pdf

"Artificial Bee Colony with Deep Belief Network based Crop Yield Estimation for Precision Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppb624-b633, October-2022, Available at : http://www.jetir.org/papers/JETIR2210188.pdf