Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks

Hirenkumar Kukadiya; Divyakant Meva; Nidhi Arora; Shilpa Srivastava

doi:10.54392/irjmt2412

Authors

Hirenkumar Kukadiya Department of Computer Application, Marwadi University, Rajkot-360003, Gujarat, India Author https://orcid.org/0000-0002-3628-5605
Divyakant Meva Department of Computer Application, Marwadi University, Rajkot-360003, Gujarat, India Author https://orcid.org/0000-0001-8804-7337
Nidhi Arora Department of Computer Science, Kalindi College, University of Delhi, Delhi-110005, India Author https://orcid.org/0009-0009-6413-8432
Shilpa Srivastava School of Sciences, Christ (Deemed to be University), Ghaziabad-201003, Delhi, India Author https://orcid.org/0000-0002-8566-1646

DOI:

https://doi.org/10.54392/irjmt2412

Keywords:

CNN Classifier, Deep Neural Network, Image Classification, Machine Learning, Groundnut, Groundnut Diseases Detection

Abstract

Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model ‘CNN8GN’. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy.

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