Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit


Authors : Arpit Patidar; Abir Chakravorty

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/46sfwext

Scribd : https://tinyurl.com/bdf7ywjd

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL195

Abstract : Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Keywords : Apple diseases, classification, convolutional neural network, deep learning, disease detection, image processing, machine learning, fruit sorting, automation.

References :

  1. Abdu, A. M., Mokji, M. M., Sheikh, U. U., & Khalil, K. (2019, March). Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves. In 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 60-64). IEEE.
  2. Adedoja, A., Owolawi, P. A., & Mapayi, T. (2019, August). Deep learning based on nasnet for plant disease recognition using leave images. In 2019 international conference on advances in big data, computing and data communication systems (icABCD) (pp. 1-5). IEEE.
  3. Al Bashish, D., Braik, M., & Bani-Ahmad, S. (2011). Detection and classification of leaf diseases using K-means-based segmentation and. Information technology journal, 10(2), 267-275.
  4. Arivazhagan, S., Shebiah, R. N., Ananthi, S., & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211-217.
  5. Arivazhagan, S., Shebiah, R. N., Ananthi, S., & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211-217.
  6. Awate, A., Deshmankar, D., Amrutkar, G., Bagul, U., & Sonavane, S. (2015, October). Fruit disease detection using color, texture analysis and ANN. In 2015 international conference on green computing and internet of things (ICGCIoT) (pp. 970-975). IEEE.
  7. Balakrishna, K., & Rao, M. (2019). Tomato plant leaves disease classification using KNN and PNN. International Journal of Computer Vision and Image Processing (IJCVIP), 9(1), 51-63.
  8. Brownlee, J. (2016). Introduction to the Python Deep Learning Library Theano. [Online]. Available.
  9. De Luna, R. G., Dadios, E. P., & Bandala, A. A. (2018, October). Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1414-1419). IEEE.
  10. Devaraj, A., Rathan, K., Jaahnavi, S., & Indira, K. (2019, April). Identification of plant disease using image processing technique. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0749-0753). IEEE.
  11. Dutot, M., Nelson, L. M., & Tyson, R. C. (2013). Predicting the spread of postharvest disease in stored fruit, with application to apples. Postharvest biology and technology, 85, 45-56.
  12. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145, 311-318.
  13. Ganatra, N., & Patel, A. (2020). A multiclass plant leaf disease detection using image processing and machine learning techniques. International Journal on Emerging Technologies, 11(2), 1082-1086.
  14. Geetharamani, G., & Pandian, A. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, 323-338.
  15. Han, J., & Moraga, C. (1995, June). The influence of the sigmoid function parameters on the speed of backpropagation learning. In International workshop on artificial neural networks (pp. 195-201). Berlin, Heidelberg: Springer Berlin Heidelberg.
  16. Ilic, M., Spalevic, P., Veinovic, M., & Ennaas, A. A. M. (2015, November). Data mining model for early fruit diseases detection. In 2015 23rd Telecommunications Forum Telfor (TELFOR) (pp. 910-913). IEEE.
  17. Jhuria, M., Kumar, A., & Borse, R. (2013, December). Image processing for smart farming: Detection of disease and fruit grading. In 2013 IEEE second international conference on image information processing (ICIIP-2013) (pp. 521-526). IEEE.
  18. Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, 59069-59080.
  19. Kim, M. S., Lefcourt, A. M., Chen, Y. R., & Tao, Y. (2005). Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion. Journal of food engineering, 71(1), 85-91.
  20. Kleynen, O., Leemans, V., & Destain, M. F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of food engineering, 69(1), 41-49.
  21. Kulkarni, O. (2018, August). Crop disease detection using deep learning. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-4). IEEE.
  22. Kumar, A., & Gill, G. S. (2015, May). Automatic fruit grading and classification system using computer vision: A review. In 2015 Second International Conference on Advances in Computing and Communication Engineering (pp. 598-603). IEEE.
  23. Lee, S. H., Wu, C. C., & Chen, S. F. (2018). Development of image recognition and classification algorithm for tea leaf diseases using convolutional neural network. In 2018 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  24. Liu, B., Zhang, Y., He, D., & Li, Y. (2017). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11.
  25. Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378-384.
  26. Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke,
  27. E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21-30.
  28. Mehl, P. M., Chao, K., Kim, M., & Chen, Y. R. (2002). Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis. Applied engineering in agriculture, 18(2), 219.
  29. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, 215232.
  30. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(7), 971-987.
  31. Patel, S., Jaliya, U. K., & Patel, P. (2020). A survey on plant leaf disease detection. Int.J. Mod. Trends Sci. Technol, 6, 129-134.
  32. Qin, F., Liu, D., Sun, B., Ruan, L., Ma, Z., & Wang, H. (2016). Identification of alfalfa leaf diseases using image recognition technology. PloS one, 11(12), e0168274.
  33. Sahithya, V., Saivihari, B., Vamsi, V. K., Reddy, P. S., & Balamurugan, K. (2019, April). GUI based detection of unhealthy leaves using image processing techniques. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0818-0822). IEEE.
  34. Soni, P., & Chahar, R. (2016, July). A segmentation improved robust PNN model for disease identification in different leaf images. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-5). IEEE.
  35. Suma, V., Shetty, R. A., Tated, R. F., Rohan, S., & Pujar, T. S. (2019, June). CNN based leaf disease identification and remedy recommendation system. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 395-399). IEEE.
  36. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  37. Tm, P., Pranathi, A., SaiAshritha, K., Chittaragi, N. B., & Koolagudi, S. G. (2018, August). Tomato leaf disease detection using convolutional neural networks. In 2018 eleventh international conference on contemporary computing (IC3) (pp. 1-5). IEEE.
  38. Waghmare, H., Kokare, R., & Dandawate, Y. (2016, February). Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In 2016 3rd international conference on signal processing and integrated networks (SPIN) (pp. 513-518). IEEE.
  39. Wu, C., Luo, C., Xiong, N., Zhang, W., & Kim, T. H. (2018). A greedy deep learning method for medical disease analysis. IEEE Access, 6, 20021-20030

Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Keywords : Apple diseases, classification, convolutional neural network, deep learning, disease detection, image processing, machine learning, fruit sorting, automation.

Video Explanation for Published paper

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