Automatic Crop Expert System Using Improved LSTM with Attention Block

作     者：Shahbaz Sikandar Rabbia Mahum Suliman Aladhadh 

作者机构：Computer Science DepartmentUniversity of Engineering and TechnologyTaxilaPakistan Department of Information TechnologyCollege of ComputerQassim UniversityBuraydahSaudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第11期

页      面：2007-2025页

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Qassim University, QU Deanship of Scientific Research, King Saud University 

主　　题：Crop recommendation LSTM deep neural network 

摘      要：Agriculture plays an important role in the economy of any *** half of the population of developing countries is directly or indirectly connected to the agriculture *** farmers do not choose the right crop for cultivation depending on their soil type,crop type,and climatic requirements like *** wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the *** parameters should be observed such as soil characteristics,type of crop,and environmental factors for the cultivation of the right *** decision-making is time-taking and requires extensive ***,there should be an automated system for the right crop recommendation to reduce human efforts and *** automated crop recommender system should take these parameters as input and suggest the farmer’s right ***,in this paper,a long short-term memory Network with an attention block has been *** proposed model contains 27 layers,the first of which is a feature input *** exist 25 hidden layers between them,and an output layer completes the *** these levels,the proposed model enables a successful recommendation of the ***,the dropout layer’s regularization properties aids in reduction of overfitting of the *** this paper,a customized novel long short-term memory(LSTM)model is proposed with a residual attention block that recommends the right crop to *** metrics used for the proposed model include f1-score,recall,precision,and accuracy attaining values as 95.69%,96.56%,96.9%,and 97.26%respectively.