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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020 ISSN 2277-8616

Classification And Detection Of Nutritional

Deficiencies In Coffee Plants Using Image

Processing And Convolutional Neural Network

(CNN)

Khenilyn P. Lewis, Juancho D. Espineli

Abstract: Coffee farmers or growers have difficulty in classifying nutritional deficiencies in coffee plants. Proper detection of these nutritional
deficiencies could help them in giving proper intervention to plants. The study was conducted to classify and detect the nutritional deficiencies in coffee
plants using image processing and Convolutional Neural Network (CNN). Once the nutritional deficiency is identified, the prototype will display
recommended fertilizer for the plant. One thousand images with eight nutritional deficiencies were used in the study namely, Boron (B), Calcium (Ca),
Iron (Fe), Nitrogen (N), Phosphorus (P), Potassium (K), Magnesium (Mg) and Zinc (Z). The study covered the four varieties of coffee named Arabica,
Robusta, Excelsa and Liberica existing in the Philippines. The capturing of images for testing and training the dataset were done in coffee farms and
nurseries in Cavite State University, National Coffee Research, Development and Extension Center (NCRDEC) and Amadeo, Cavite. Experimental and
development research designs were used. Image processing techniques was utilized in converting the images into grayscale and binary values for
threshold and segmentation. Convolutional Neural Network (CNN) provides the predicted nutritional deficiencies in the coffee plants through
classification and detection. Results shows that CNN has a high accuracy in detecting and classifying the nutritional deficiencies in coffee plants. The
prototype was evaluated, and results shows that it is an effective alternative for classifying and detecting the nutritional deficiencies in coffee plants.

Index Terms: classification, coffee, convolutional neural network, detection, image processing, machine learning, nutritional deficiencies
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1 INTRODUCTION Adequate nutrients are essential to plants for growth. During
Coffee is known as the most important crop commodity in the the vegetation to pre-flowering of coffee plants, Nitrogen and
world [1]. People around the world drink two billion cups of Potassium promote the growth of tissues in new plants.
coffee every day. A total of 25 million of families depend for Calcium is for leaf growth that provides high yield for plants.
coffee for living specifically for business or as growers. For the Magnesium serve as fuel for developing tissues and Sulfur to
last 15 years, 43 percent of coffee consumption has arisen maximize the growth of the plant through protein. During the
worldwide [2]. The first coffee tree in Philippines was planted post flowering to berry formation, Nitrogen and Potassium
in Lipa, Batangas and in 1860s coffee are exported to serve as maintenance for plant growth and support berry
America. Batangas reigned the coffee industry in the country strength. Calcium is for strong healthy tissues. Magnesium
and was followed by Cavite in planting coffee in 1876. and micronutrients are for growth maintenance and berry
However, due to insect manifestation coffee trees in Batangas production. These nutrients are needed by the plants in their
was destroyed and few of surviving trees was transferred to berry expansion and berry maturity [9]. These nutrients are in
Cavite. As of 2019, Batangas produces 13 percent of the need necessary for the growth of coffee plants. A healthy
coffee supply in CALABARZON (Region IV-A) and Cavite coffee plants could maximize the number of yields that may
produces 67 percent [3]. The climate and soil condition in the produce. However, identification of nutritional deficiencies is
Philippines are suitable for planting coffee. Because of this, done manually by the coffee growers or experts.
the country could produce the four varieties of coffee namely, Characteristics and symptoms of plants in terms of nutritional
Arabica (Coffea arabica), Liberika (Coffea liberica), Excelsa deficiencies are usually like other plants. Coffee growers or
and Robusta [5]. Arabica is the most expensive variety of farmers should have enough knowledge to these symptoms so
coffee and usually cultivated in high elevation areas. Liberika that they could perform the correct interventions [12].
(Kapeng Barako) is known for strong flavor and aroma. Numerous applications of digital image processing have been
Excelsa has bigger berries compared to Arabica. Robusta is recorded in different field. Digital image processing is the
being used for expresso and instant coffee mixes. Moreover, manipulation of images using the computer [10]. It converts
Robusta is the commonly grown variety coffee in country the physical images into corresponding images and extract the
which has a total production of 69 percent in 2015. Followed information using algorithms. Digital image processing
by Arabica which contributed 24 percent and Excelsa and includes image collection, image processing and image
Liberica [6]. analysis [11]. This study was conducted to utilize an image
processing technique, implement Convolutional Neural
______________________________________ Network (CNN) and measure the effectiveness for classifying
and detecting nutritional deficiencies in coffee plants.
• Khenilyn P. Lewis is currently pursuing doctor in information
technology in AMA University-Quezon City, Philippines. She is also 2 RELATED WORK
working as a faculty member in Cavite State University, Cavite, This section discusses the related works in image processing
Philippines. E-mail: khenilyn@yahoo.com
• Juancho D. Espineli is currently the dean of the school of graduate and algorithms as basis for classifying and detecting the
studies in AMA University- Quezon City. E-mail: nutritional deficiencies in coffee plants. A neural network was
jcespineli@gmail.com trained to detect the nutritional deficiencies in coffee plants. As
a result, a Kappa coefficient of 0.96 for Nitrogen and
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