Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches

Houda Orchi (), Mohamed Sadik, Mohammed Khaldoun and Essaid Sabir
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Houda Orchi: NEST Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 20000, Morocco
Mohamed Sadik: NEST Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 20000, Morocco
Mohammed Khaldoun: NEST Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 20000, Morocco
Essaid Sabir: NEST Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 20000, Morocco

Agriculture, 2023, vol. 13, issue 2, 1-35

Abstract: With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of plant pathogens as well as a lot of work, and excessive processing time. For these purposes, various methods based on image processing, deep learning, and machine learning are developed and examined by researchers for crop leaf disease identification and often have obtained significant results. Motivated by this existing work, we conducted an extensive comparative study between traditional machine learning (SVM, LDA, KNN, CART, RF, and NB) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50, and CNN) models in terms of precision, accuracy, f1-score, and recall on a dataset taken from the PlantVillage Dataset composed of diseased and healthy crop leaves for binary classification. Moreover, we applied several activation functions and deep learning optimizers to further enhance these CNN architectures’ performance. The classification accuracy (CA) of leaf diseases that we obtained by experimentation is quite impressive for all models. Our findings reveal that NB gives the least CA at 60.09%, while the InceptionV3 model yields the best CA, reaching an accuracy of 98.01%.

Keywords: traditional machine learning; deep learning; crop disease detection; classification accuracy; deep learning optimizers; activation functions (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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