Crop Recommendation for Better Crop Yield for Precision Agriculture Using Ant Colony Optimization with Deep Learning Method

Abstract

PAG (Precision Agriculture) is managing farms with the use of IT (Information Technology). PAG can monitor and administer required health to crops and soil thus helping productivity. Agriculturists use traditional recommender systems for farming. This research work proposes a DLT (Deep Learning Technique) recommender system for crops. This study uses gathered historical data of crops and climate for its recommendations. This work’s proposed scheme is a hybrid scheme that uses ACOs (Ant Colony Optimizations) for optimizing DCNN (Deep convolution Neural Networks) and LSTM (Long Short Term Memory) network inputs called (ACO-IDCNN-LSTM) for crop predictions. DCNNs generally achieve high levels of accuracy but involve computational complexities based on the count of layer used in processing. DCNNs adding of weights in its nodes are a major part of complexity increments and hence this work adjusts these weights in training to reduce complex processing. ACOs optimize hyper parameters in training to help reduce complexity in weights and for DCNN predictions on crops. The recommender system produced satisfactory results.