System for Managing Pesticide Recommendation on the Cotton Crop using Deep Learning Techniques VGG and Xgboost

Abstract

This study seeks to bring about a transformative impact on the agriculture industry via the use of cutting-edge technology to enhance the efficiency of pesticide recommendations for cotton crops. This work explores the enhancement of accuracy and reliability in pesticide recommendations for optimal crop management by using Deep Learning models such as VGG (Visual Geometry Group) and the ensemble learning method XGBoost. This study investigates the possibilities of using Deep Learning methods, namely VGG16 and VGG19, which are well recognized for their exceptional performance in picture recognition tasks. The objective is to examine their applicability in the domain of proposing optimal pesticides for effectively managing cotton crop diseases. Furthermore, the integration of the XGBoost algorithm, renowned for its resilience and exceptional generalization capabilities, serves to augment the forecast accuracy within this particular field. The assessment of different models provides valuable insights into their efficacy within the framework of pesticide prescription systems for cotton cultivation. Both the VGG16 and VGG19 models consistently and robustly demonstrate predictive skills, which provide promising results for pesticide prescription. The XGBoost model demonstrates high dependability and robust prediction accuracy, making a substantial contribution to the objectives of the research. Nevertheless, the model under consideration demonstrates outstanding Precision and Recall, but it does show a little trade-off in terms of total Accuracy. This discovery implies the need for more modification of the model in order to establish a well-balanced recommendation system that preserves the outstanding precision seen, while also enhancing overall accuracy. The integration of Deep Learning methodologies, namely VGG models, with the ensemble learning methodology of XGBoost, offers a novel avenue for enhancing the optimization of pesticide recommendations in cotton crop management. This study presents opportunities for the advancement of more resilient and precise systems via the integration of cutting-edge technology, therefore facilitating the adoption of more effective and environmentally friendly farming methods. The suggested model exhibits potential for improving the precision and dependability of pesticide recommendations for cotton crops via the use of Deep Learning and ensemble learning approaches.