Abstract
Effective disease control and agricultural production require accurate and rapid crop disease detection. The worldwide commodity chili crops are sensitive to several diseases, including anthracnose, which reduces yields and harms farmers. Traditional disease detection approaches are laborious, time-consuming, and require specialized knowledge, adding to intervention delays and economic losses. The lack of systematic chili disease data makes identification more difficult. This research attempts to improve agricultural disease identification utilizing feature fusion, transfer learning, and a Convolutional Neural Network (CNN) to accurately and effectively diagnose chili plant anthracnose disease. Images are represented by two feature extractors: the first is the CNN based on VGG19, and the second is the Hybrid Feature Extractor (HFE). Three feature extraction techniques—Speed Up Robust Feature (SURF), Local Binary Pattern (LBP), and Histogram-Oriented Gradient (HOG) are combined into a single fused feature vector by the HFE. The classification model is then created by combining these two feature vectors. Using this combined feature set, a CNN with a fully connected layer and SoftMax function is trained to identify whether chili images are healthy or unhealthy. The model is also improved and optimized through data augmentation. The feature fusion approach shows great promise because it can more precisely detect anthracnose disease in chilli plants. Using 128 x 128 pixel images, the model learned at a rate of 0.01 and achieved 99.58% success after 100 iterations. Regardless of different batch sizes and learning rates, the model performs well. When compared to the top models currently in use, the feature fusion approach produces better performance results. The financial loss caused by anthracnose disease and the research on managing chili crops will benefit sustainable agriculture.
References
Sahid, Zulfikar Damaralam, Muhamad Syukur, Awang Maharijaya, and Waras Nurcholis. "Quantitative and qualitative diversity of chili (Capsicum spp.) genotypes." Biodiversitas Journal of Biological Diversity 23, no. 2 (2022).
Saini, Tejbhan Jalsingh, Ganapati Bhat, Anshuman Tiwari, Shantikumar Gupta, and Radhamani Anandalakshmi. "Identification, prevalence and pathogenicity of Colletotrichum species associated with chilli anthracnose in India." Journal of Plant Pathology 107, no. 1 (2025): 3-22.
Singh, Vivek, U. K. Tripathi, Abhishek Singh, Ashwani Kumar Patel, and Mukesh Kumar. "Exploring Chilli Anthracnose in Uttar Pradesh's Key Cultivation Regions." Int. J. Plant Soil Sci 35, no. 19 (2023): 2265-2270.
Chin, Ruben, Cagatay Catal, and Ayalew Kassahun. "Plant disease detection using drones in precision agriculture." Precision Agriculture 24, no. 5 (2023): 1663-1682.
Miller, Sally A., Anna L. Testen, Jonathan M. Jacobs, and Melanie L. Lewis Ivey. "Mitigating emerging and reemerging diseases of fruit and vegetable crops in a changing climate." Phytopathology® 114, no. 5 (2024): 917-929.
Yang, Li-Na, Maozhi Ren, and Jiasui Zhan. "Modeling plant diseases under climate change: evolutionary perspectives." Trends in plant science 28, no. 5 (2023): 519-526.
Miao, Ke & Chen, Chenglei & Zheng, Xianqing. (2023). A study on the application of artificial intelligence in the design of intelligent medical robots. Applied Mathematics and Nonlinear Sciences. 9. 10.2478/amns.2023.2.01388.
Mangalika, Udula. "Object Recognition to Content Based Image Retrieval: A Study of the Developments and Applications of Computer Vision." (2024).
Sajitha, P., A. Diana Andrushia, N. Anand, and Mohannad Z. Naser. "A review on machine learning and deep learning image-based plant disease classification for industrial farming systems." Journal of Industrial Information Integration 38 (2024): 100572.
Dyke, Roberto M., and Kai Hormann. "Histogram equalization using a selective filter." The visual computer 39, no. 12 (2023): 6221-6235.
Yoshihara, Akira, Kazuki Fujikawa, Kazuhiro Seki, and Kuniaki Uehara. "Predicting stock market trends by recurrent deep neural networks." In Pacific rim international conference on artificial intelligence, pp. 759-769. Cham: Springer International Publishing, 2014
Awasthi, Rohit Kumar, and Srikant Singh. "An Overview of Machine Learning Methods for the Detection of Diseases in Rice Plants in Agricultural Research." (2023).
Wani, Javaid Ahmad, Sparsh Sharma, Malik Muzamil, Suhaib Ahmed, Surbhi Sharma, and Saurabh Singh. "Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges." Archives of Computational methods in Engineering 29, no. 1 (2022): 641-677.
Yu, Lixiran, Hong Xie, Yan Xu, Qiao Li, Youwei Jiang, Hongfei Tao, and Mahemujiang Aihemaiti. "Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm." Agriculture 14, no. 10 (2024): 1693.
Gharsallah, Olfa, Claudio Gandolfi, and Arianna Facchi. "Methodologies for the sustainability assessment of agricultural production systems, with a focus on rice: a review." Sustainability 13, no. 19 (2021): 11123.
Ha, Jisu, Jun-Young Park, Yoonseok Choi, Pahn-Shick Chang, and Kyung-Min Park. "Comparative analysis of universal protein extraction methodologies for screening of lipase activity from agricultural products." Catalysts 11, no. 7 (2021): 816.
Tetila, Everton Castelão, Bruno Brandoli Machado, Gabriel Kirsten Menezes, Adair da Silva Oliveira, Marco Alvarez, Willian Paraguassu Amorim, Nícolas Alessandro De Souza Belete, Gercina Gonçalves Da Silva, and Hemerson Pistori. "Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks." IEEE geoscience and remote sensing letters 17, no. 5 (2019): 903-907.
Waheed, Abdul, Muskan Goyal, Deepak Gupta, Ashish Khanna, Aboul Ella Hassanien, and Hari Mohan Pandey. "An optimized dense convolutional neural network model for disease recognition and classification in corn leaf." Computers and Electronics in Agriculture 175 (2020): 105456.
Agarwal, Mohit, Suneet Kr Gupta, and Kanad Kishore Biswas. "Development of Efficient CNN model for Tomato crop disease identification." Sustainable Computing: Informatics and Systems 28 (2020): 100407.
Chitta, Subrahmanyasarma, Vinay Kumar Yandrapalli, and Shubham Sharma. "Deep Learning for Precision Agriculture: Evaluating CNNs and Vision Transformers in Rice Disease Classification." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, IEEE, 2024, 1-6.
Pandey, Priyanshu, and Rusha Patra. "A Real-time Web-based Application for Automated Plant Disease Classification using Deep Learning." In 2023 IEEE International Symposium on Smart Electronic Systems (iSES), IEEE, 2023, 230-235.
Moupojou, Emmanuel, Appolinaire Tagne, Florent Retraint, Anicet Tadonkemwa, Dongmo Wilfried, Hyppolite Tapamo, and Marcellin Nkenlifack. "FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning." IEEE Access 11 (2023): 35398-35410.
Al-Gaashani, Mehdhar SAM, Nagwan Abdel Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Asadulla Ashurov, and Mohammed Saleh Ali Muthanna. "Deep transfer learning with gravitational search algorithm for enhanced plant disease classification." Heliyon 10, no. 7 (2024).
[Preethi, P., R. Swathika, S. Kaliraj, R. Premkumar, and J. Yogapriya. "Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification." Food and Energy Security 13, no. 5 (2024): e70001.
Ojo, Mike O., and Azlan Zahid. "Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline." Agronomy 13, no. 3 (2023): 887.
Alhwaiti, Yousef, Muntazir Khan, Muhammad Asim, Muhammad Hameed Siddiqi, Muhammad Ishaq, and Madallah Alruwaili. "Leveraging YOLO deep learning models to enhance plant disease identification." Scientific Reports 15, no. 1 (2025): 7969.
Adnan, Faiqa, Mazhar Javed Awan, Amena Mahmoud, Haitham Nobanee, Awais Yasin, and Azlan Mohd Zain. "EfficientNetB3-adaptive augmented deep learning (AADL) for multi-class plant disease classification." IEEE Access 11 (2023): 85426-85440.
Srinivasulu, Maramreddy, and Sandipan Maiti. "RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification." Engineering Research Express 6, no. 3 (2024): 035204.
“Anthracnose Disease in Chili.” Accessed: Apr. 20, 2025.[Online]. Available: https://www.kaggle.com/datasets/prudhvi143413s/anthracnose-disease-in-chili-palnadu-ap
Agustina, Ina, Fauziah Nasir, and Anggit Setiawan. "The implementation of image smoothing to reduce noise using Gaussian filter." (2017).
Admass, Wasyihun Sema, Yirga Yayeh Munaye, and Girmaw Andualem Bogale. "Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification." International Journal of Information Technology 16, no. 2 (2024): 817-829.
Wu, Hao, Lincong Fang, Qian Yu, and Chengzhuan Yang. "Composite descriptor based on contour and appearance for plant species identification." Engineering Applications of Artificial Intelligence 133 (2024): 108291.
Mulcahy, Colm. "Image compression using the Haar wavelet transform." Spelman Science and Mathematics Journal 1, no. 1 (1997): 22-31.
Probierz, Eryka. "On emotion detection and recognition using a context-aware approach by social robots: modification of faster R-CNN and YOLO v3 neural networks." (2023).
Chen, Junde, Defu Zhang, Md Suzauddola, and Adnan Zeb. "Identifying crop diseases using attention embedded MobileNet-V2 model." Applied Soft Computing 113 (2021): 107901.
Likhar, Kiran, and Sonali Ridhorkar. "Enhancing skin cancer detection: A comparative analysis of models with VGG-16, VGG-19, and inception V3." International Journal of Intelligent Systems and Applications in Engineering 12 (2024): 502-514.
Nawar, Abbas Khalifa, Hadi Raheem Ali, Mothefer Majeed Jahefer, and Sabah Abdulazeez Jebur. "Automate facial paralysis detection using vgg architectures." ijciar. v7i1 158.
Zheng, Yufeng, Clifford Yang, and Alex Merkulov. "Breast cancer screening using convolutional neural network and follow-up digital mammography." In Computational Imaging III, vol. 10669, 1066905. SPIE, 2018.