AJCS 18(07):408-415 (2024) ISSN:1835-2707
https://doi.org/10.21475/ajcs.24.18.07.p4037
Convolutional neural network as an efficient alternative to supervised
learners for modelling plant diseases
Sridevy Sridarane
1
, Praveena Sivalogeswaran
2
, Nirmala Devi
Muthusamy
3
, Hema Bharathi Chinnaswamy
4
, Kumaresan
Palaniyappan
5
, Djanaguiraman Maduraimuthu
6
, S. Rajabathar
7
and
Natarajan Balakrishnan
8
*
1
Department of Physical Science & Information Technology, Tamil
Nadu Agricultural University, Coimbatore-3, India
2
Teaching Faculty, Food and Agribusiness Management, NIFTEM-T,
Thanjavur- 5, India
3
Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil
Nadu, India
4
Centre for Water and Geospatial Studies, Tamil Nadu Agricultural
University, Coimbatore-3, India
5
Department of Crop Physiology, Tamil Nadu Agricultural University,
Coimbatore-3, India
6
Kanchi Mamunivar Government Institute for Postgraduate Studies and
Research, Puducherry -8, India
7
Teaching Assistant, AC & RI, Tamil Nadu Agricultural University,
Coimbatore-3, India
Abstract: The high productivity of the agricultural sector is crucial to
the Global economy, as well as the national economy. For instance,
India increased its nation's GDP by a factor of 17% to 18% in the past
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few decades. In India, the farming sector is the primary source of
income. Numerous insects and illnesses have an impact on plant
development, amount, and quality of products. Therefore, it is crucial
to find the diseases early in the plant's development. Image
processing is used to identify plant diseases and pests. Artificial
intelligence, machine learning, and visual analysis have all been used
in the past few decades to find and diagnose plant diseases. These
computerized techniques are excellent for quickly monitoring vast
acreage. In this study, we used the foliage of potato plants to identify
diseased leaves affected by early blight, as well as late blight. A
Comparison is thus laid between the Convolutional Neural Network
(CNN) Model for this purpose, and the standard Supervised Learning
Classifiers, like
k
– Nearest Neighbors, Support Vector Machine,
AdaBoost, etc. The comparison is subjected not only towards the
traditional Metrics, like Accuracy, Precision, etc. but is expanded to
temporal domains as well, that is the time taken to compute the
results. Also, the training, and testing split used while incorporating
the model is another deciding factor in its performance. Several such
training–testing splits are considered for such, and thereby, the
convolutional Neural Network Model is found to be better in
performance than the Supervised Machine Learners irrespective of the
Splitting Ratio of the Training, and the Testing Data.
Keywords: Machine Learning, Supervised Learning, Artificial Neurons,
Solanum Tuberosum.
Introduction
India is a country of farmers; agribusiness and productive employment
account for over sixty per cent of the country's workforce. India's
agricultural industry is reliant on the monsoon months Agriculture
produces greater quantities when the rainy season is favourable; while
when it is bad, farming produces fewer crops or is not in good health.
India had the second-largest populace in the world. As a result of
expanding populations, farm holdings have been broken up and
disorganized, making them unprofitable. Agriculture cultivation in
India employs conventional Zhang et al. (2022) techniques and
conventional machinery. This is a result of the destitution of
individuals and ignorance. Farmers planted a variety of crops on their
farms. Here, crops occupy 75% of the land used for cultivation and
commercial crops occupy only the remaining 25%. India has two
cropping seasons – Rabi, and Kharif, and the monsoon is solely
responsible for such. All region's harvest trends are influenced by a
variety of variables, including the environment, climate, the system of
irrigation, financial drivers, facilities, and social engagement.
Agricultural issues are numerous. In India, farming practices remained
self-sustaining, but output is still reliant on resources, cereal-centric,
and often geographically Gharib and Mandour (2022) biased, raising
sustainability concerns. Farmers have been using increasing amounts
of pesticides and fertilizers to boost crop yield, but these methods
come with a couple of drawbacks; they may negatively affect human
health and make people sick; and Bugs will eventually acquire a
resistance to the chemicals, rendering them ineffectual over time. 10%
of the freshwater we use is for cultivation. New industrial methods
need additional water. The manufacturing process of the main crops
in India temporarily stops. Due to an enormous discrepancy amid the
demands and supplies of the nation's growing population and
manufacturing, legislation administrators and administrators are
concerned. On one farming operation, the landowner continually
harvested the same crop, making it less fertile. Studying the
development of vegetation necessitates monitoring of its chemical and
physical characteristics. Chlorophyll Najm et al. (2012) as well as