Identification of Macro-Nutrient Deficiency in Onion Leaves (Allium cepa L.) Using Convolutional Neural Network (CNN)

In the Philippines, the onion is among the major crops produced with an estimated production of 229.50 thousand metric tons last 2020. Producing these onions relies on manpower, particularly from local farmers. However, the nutrient deficiency that the onions may have can affect the product quality and crop yield. The possible nutrient deficiency of onions may be identified based on the features of the leaves. Therefore, based on the visual symptoms, a modern approach to identifying the primary macro-nutrient deficiency in onion leaves was developed using a Convolutional Neural Network with the VGG-16 architecture. The system obtained an accuracy of 60%, 76%, and 100% in identifying the macro-nutrient deficiency of onion leaves in Nitrogen, Phosphorus, and Potassium, respectively. It obtained an accuracy of 96% in determining no nutrient deficiency in onion leaves. The overall system accuracy obtained was 83%.

[1]  J. Villaverde,et al.  A Fuzzy Logic Approach for the Determination of Carabao Mango Ripeness Level and Shelf Life , 2022, 2022 IEEE Region 10 Symposium (TENSYMP).

[2]  J. Srivastava,et al.  Cassava Leaf Disease Detection Using Deep Learning , 2022, 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).

[3]  N. Linsangan,et al.  Detection and Identification of Abaca Diseases using a Convolutional Neural Network CNN , 2021, TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON).

[4]  N. Linsangan,et al.  Classification of Healthy and Unhealthy Abaca leaf using a Convolutional Neural Network (CNN) , 2021, 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM).

[5]  J. Villaverde,et al.  Portable Sigatoka Spot Disease Identifier on Banana Leaves Using Support Vector Machine , 2021, 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM).

[6]  J. Villaverde,et al.  Robusta Coffee Leaf Detection based on YOLOv3- MobileNetv2 model , 2021, 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM).

[7]  N. Rajathi,et al.  Apple and Grape Leaf Disease Classification using MLP and CNN , 2021, 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA).

[8]  J. Villaverde,et al.  A Durian Variety Identifier Using Canny Edge and CNN , 2021, 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE).

[9]  Ankith I.  Convolutional Neural Network for Image Recognition , 2021, International Journal for Research in Applied Science and Engineering Technology.

[10]  G. Magwili,et al.  Determination of Shelled Corn Damages using Colored Image Edge Detection with Convolutional Neural Network , 2020, 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM).

[11]  Dionis A. Padilla,et al.  Detection of Corn Leaf Diseases Using Convolutional Neural Network With OpenMP Implementation , 2020, 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM).

[12]  Wen-Yaw Chung,et al.  Integration of Water Control with a Drip Irrigation System for Agricultural Application , 2019, 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ).

[13]  Ricardo ALASCO,et al.  SoilMATTic: Arduino-Based Automated Soil Nutrient and pH Level Analyzer using Digital Image Processing and Artificial Neural Network , 2018, 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).

[14]  Ukrit Watchareeruetai,et al.  Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks , 2018, 2018 International Electrical Engineering Congress (iEECON).

[15]  Tareq Abed Mohammed,et al.  Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).

[16]  N. Linsangan,et al.  Plant Identification by Image Processing of Leaf Veins , 2017, ICISPC 2017.

[17]  P. B. Sowmiya,et al.  Design of Automatic Nutrition Supply System Using IoT Technique in Modern Cities , 2017, 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC).

[18]  P. Derfler,et al.  The United States Department of Agriculture , 1872, Nature.