CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions

Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam and Hanif Bhuiyan ()
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Mohammad Badhruddouza Khan: Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh
Salwa Tamkin: Department of Computer Science and Engineering, Brac University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh
Jinat Ara: Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem u. 10, 8200 Veszprem, Hungary
Mobashwer Alam: Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia
Hanif Bhuiyan: Monash Data Futures Institute, Monash University, Clayton Campus, Clayton, VIC 3800, Australia

Data, 2025, vol. 10, issue 2, 1-22

Abstract: Crop failure is defined as crop production that is significantly lower than anticipated, resulting from plants that are harmed, diseased, destroyed, or influenced by climatic circumstances. With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. However, deep learning seems to be a largely complex black box to interpret, necessitating a prerequisite for the groundwork of the model’s interpretability. Considering this, the aim of this study was to follow up on the establishment of a robust deep learning custom model named CropsDisNet, evaluated on a large-scale dataset named “New Bangladeshi Crop Disease Dataset (corn, potato and wheat)”, which contains a total of 8946 images. The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. In addition, demonstration of the model’s interpretability gave us insight into our model’s decision making and classification results, which can allow farmers to understand and take appropriate precautions in the event of early widespread harvest failure and food crises.

Keywords: Crops Disease Detection; custom CNN model; deep learning in agriculture; privacy preserve Net; privacy protection in agriculture (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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