Potato Leaf Disease Detection Through Image Processing Techniques I InformativeBD

Rokon Uz Zaman, KU Ahmmad , Debasis Sarkar , MAA Mumin , and N Salahin, from the different institute of the Bangladesh. wrote a research article about, Potato Leaf Disease Detection Through Image Processing Techniques. Entitled, Potato leaf disease detection using image processing. This research paper published by the International Journal of Agronomy and Agricultural Research (IJAAR). an open access scholarly research journal on Agronomy. under the affiliation of the International Network For Natural Sciences | INNSpub. an open access multidisciplinary research journal publisher.

Abstract 

Agriculture is one of the most important pillars of Bangladesh’s economy. However, due to some factors such as plant diseases, pests, climate change, the yield of the farming industry decreases, and the productivity decreases as well. The detection of plant diseases is crucial to avert the losses in the productivity and in the yield. It is not obvious to monitor the plant diseases manually as the act of disease detection is very critical. It needs a huge effort, along with knowledge of plant diseases and extensive processing times. Therefore, image processing technology is used to detect the plant disease, this is done by capturing the input image that undergoes the process and is compared with the dataset. This dataset is composed of diverse diseases of potato leaves in the image format.  This study aims to build a web application to predict the diseases of potato plants that will help farmers to identify the diseases so that they can use appropriate fungicide to get more yields. The purpose of this study is to assist and provide efficient support to the potato farmers. In this study, we propose a system that will use the techniques of image process to both analyze and detect the plant diseases using machine learning Conventional Neural Networks (CNN) with Tensorflow framework 2. The results of the implementation show that the designed system could give a successful result by detecting and classifying the potato leaf diseases and healthy plant.

Read more : Sensometric Evaluation of Minimally Processed Snapping Shrimp (Alpheus sp.) Cold Cuts in Aquaculture |InformativeBD

 Introduction

Agricultural industry is the backbone of our economy that contributes about 11.63% of GDP (BBS 2021). Potato is an important and leading crop in Bangladesh. Bangladesh is the seventh potato producing country in the world and ranks second after rice in terms of production and are the third most important food crop after rice and wheat in terms of human consumption in Bangladesh (FAOSTAT, 2020). According to the DAE statistics, about 9.61 million MT of potatoes have been produced in 2020 against the annual demand of about 6.82 million MT, bringing a surplus of 3.40 million MT despite some amounts is being exported (DAM, 2020). 

But in Bangladesh late blight is the most common and highly destructive, fungal disease in potato and annual potato yield losses due to late blight have been estimated at 25-57% (GEOPOTATO project report, 2016-2019). Plants are sensitive to diseases especially the plant leaves as symptoms of the disease appear first on the leaves. Due to the bad impacts of plant diseases on the both the economy and environment, the farmers should consider monitoring the crops in such a way that they may mitigate losses. It exists a way that is used by experts to monitor the crops which is the naked eye observation. This is a traditional method that has many constraints related to time consuming as the operation of monitoring is done manually, and it requires the presence of experts. However, lately, crop monitoring is being developed to be digital and semi-automatic, meaning that only from the symptoms that are shown on the leaf, the disease could be detected in an easier, quicker, cheaper way. Therefore, this digitalized method will be beneficial for the farmers as well since it will facilitate for them the detection of the diseases because most of the farmers do not have a sufficient background and knowledge about monitoring the crops and dealing with the variety of diseases that could affect them. There are many researcher reported, leaf dieses classification and detect is successfully possible by using image processing techniques of deep learning as well as machine learning. Different methods for machine learning and deep learning include the Support Vector Machines (SVM), Random. 

Forests (RF), K-nearest Neighbor (KNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), along with models such as AlexNet, GoogleNet, and Caffe are used to classify and detect to leaf dieses (Knaak et al., 2021). The report presented a machine-learning model including canny edge detection technique for edge feature extraction, grid color movement for extracting color features and local binary pattern (LBP) for texture analysis. Where the features were extracted combined to create a combined feature vector which was used for training the artificial neural network (ANN). The convolutional model is also capable of differentiating the plant leaves and recognizing rice plants and their diseases (Shrivastava et al., 2022). Potato leaf diseases were detected by using random forest classifiers where image pre-processing was done in two steps like image normalization and color space conversion where segmentation was done using thresholding HSV images in RGB color space and global feature descriptor (GFD), gray level cooccurrence matrix (GLCM), color histogram were used for extracting features. Finally, classification was done using random forest (RF) classifiers (Iqbal et al., 2020). The proposed system that we are suggesting in this paper could be used by the farmers to increase the yield with no need to consult experts. The core purpose of this proposed system is not aiming only at detecting the plant diseases using the image processing technology, but it aims also at directing the user farmer to use a mobile application in which he will upload the image and receive the type of disease infection along with a suggestion of needed pesticides. The digitalization of the agriculture field has known the intervention of the latest technologies namely the image processing. As a result, our system that is designed to be automated system is implemented using image processing technique using machine learning Convolutional Neural Networks (CNN) with Tensorflow Framework 2.

Reference

BBS. 2021. Report of the Share of economic sectors in the GDP in Bangladesh.

DAM. 2020. Report of the Department of Agricultural Marketing, Khamarbari, Dhaka,    Bangladesh.

FAOSTAT. 2020. New food balance sheet for Bangladesh. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#data/FBS.

GEOPOTATO. 2016-2019. Report of the GEOPOTATO. https://www.wur.nl/en/project/geopotato-control-fungal-disease-in-potato-in-bangladesh.htm

Iqbal MA,  Talukder KH.  2020. Detection of Potato Disease Using Image Segmentation and Machine Learning. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 43–47. DOI: 10.1109/WiSPNET48689.2020.9198563

Knaak C, Von Eßen J, Kröger M, Schulze F, Abels P,  Gillner A. 2021. A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards. Sensors 21(12), 4205, DOI: 10.3390/s21124205.

Shrivastava G, Patidar H. 2022. Rice Plant Disease Identification Decision Support Model Using Machine Learning. Ictact Journal on Soft Computing 12(3), 2619-2627. DOI: 10.21917/ijsc.2022.0375

Source : Potato leaf disease detection using image processing 

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Nile Tilapia Count and Location: AI and CLAHE Unleashed | InformativeBD

Ben Saminiano, Arnel Fajardo, and  Ruji Medina, from the different institute of the Philippines. wrote a research article about, Nile Tilapia Count and Location: AI and CLAHE Unleashed. entitled, Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and CLAHE. This research paper published by the Journal of Biodiversity and Environmental Sciences (JBES). an open access scholarly research journal on Biodiversity. under the affiliation of the International Network For Natural Sciences | INNSpub. an open access multidisciplinary research journal publisher.

Abstract

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.

Read more : Andrographis Paniculata: Exploring Medicinal Marvels | InformativeBD

Introduction

Accurate counting of organisms, such as Nile Tilapia (Oreochronis niloticus), is important for various applications, including fisheries management, environmental monitoring, and aquaculture operations (Li et al., 2020). In the Philippines, tilapia is the second most important cultured species, with approximately 281,111 MT of total production in 2021. In 2020, tilapia made up 20% of the aquaculture production in the country, with Central Luzon as the leading producer. Tilapia is an important commodity for food security and economic development (PCAARRD, n.d.).

The tilapia industry in the Philippines has made notable growth in production from 2002 to 2022, with an increase of 115.58%. This may be attributed to several programs done by the government, such as improving the strain of tilapia and improving the technology in production and culture to sustain industry growth (Bureau of Fisheries and Aquatic Resources, 2022).

However, despite the progress made in tilapia aquaculture, problems and challenges persist. Pollutionrelated problems like diseases and water quality management, sources of quality fingerlings, and market competition are among the key challenges faced by farmers (Bureau of Fisheries and Aquatic Resources, 2022). Addressing these challenges and enhancing the efficiency and sustainability of tilapia production is crucial for the industry's continued growth.

In this context, developing an automated methodology for accurate surface tilapia detection using a Convolutional Neural Network (CNN) brings an opportunity to improve tilapia farming practices. Leveraging the capabilities of CNN and Contrast Limited Adaptive Histogram Equalization (CLAHE) aims to develop an approach to determine whether Nile Tilapia are at the surface or submerged. The insights gained from this research can contribute to optimizing feeding strategies, improving management practices, and enhancing tilapia aquaculture's overall productivity.

The paper is presented as follows: Section 1 introduces the motivation for the research. Section 2 concentrated on the related works on image processing, CNN, and CLAHE. The methodology of the research is presented in Section 3. Section 4 presents the Tests and Results. Finally, Section 5 discussed the conclusion and future works.

Reference

Bureau of Fisheries and Aquatic Resources. 2022. The Philippine Tilapia Industry Roadmap (2022-2025).

Conrady CR, Er Ş, Attwood CG, Roberson LA, de Vos L. 2022. Automated detection and classification of southern African Roman seabream using mask R-CNN. Ecological Informatics 69, 101593.

Jose JA, Kumar CS, Sureshkumar S. 2022. Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models. Information Processing in Agriculture 9(1), 68–79.

Li D, Miao Z, Peng F, Wang L, Hao Y, Wang Z, Chen T, Li H, Zheng Y. 2020. Automatic counting methods in aquaculture: A review.

Lumauag R, Nava M. 2019. Fish tracking and counting using image processing. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, 1-4. https://doi.org /10.1109/HNICEM.2018.8666369

Mandal R, Connolly RM, Schlacher TA, Stantic B. 2018. Assessing fish abundance from underwater video using deep neural networks. In Proceedings of the International Joint Conference on Neural Networks (Vols. 2018-July). https://doi.org/10.1109 /IJCNN.2018.8489482

Mishra A, Gupta M, Sharma P. 2018. Enhancement of Underwater Images using Improved CLAHE. 2018 International Conference on Advanced Computation and Telecommunication, ICACAT 5, 1-6. https://doi.org/10.1109/ICACAT.2018.8933665

Muksit AAl, Hasan F, Hasan Bhuiyan Emon MF, Haque MR, Anwary AR, Shatabda S. 2022. YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Ecological Informatics 72, 101847. https://doi.org/10.1016 /J.ECOINF.2022.101847

PCAARRD. 2023. (n.d.). Tilapia – Industry Strategic Science and Technology Plans (ISPs) Platform. Retrieved June 24, 2023, from  https://ispweb. pcaarrd. dost.gov.ph/tilapia-2/

Redmon J, Farhadi A. 2018. YOLOv3: An incremental improvement. ArXiv.

Saminiano B. 2020. Feeding Behavior Classification of Nile Tilapia (Oreochromis niloticus) using Convolutional Neural Network. International Journal of Advanced Trends in Computer Science and Engineering 9(1.1 S I), 259–263. https://doi.org /10.30534/ijatcse/2020/4691.12020

Wang H, Zhang S, Zhao S, Wang Q, Li D, Zhao R. 2022. Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. Computers and Electronics in Agriculture 192, 106512. https://doi.org/10.1016 /J.COMPAG.2021.106512

Yu C, Fan X, Hu Z, Xia X, Zhao Y, Li R, Bai Y. 2020. Segmentation and measurement scheme for fish morphological features based on Mask R-CNN. Information Processing in Agriculture 7(4), 523–534. https://doi.org/10.1016/J.INPA.2020.01.002

Source : Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and  CLAHE 

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