Fruit Quality Prediction using Deep Learning Strategies for Agriculture


  • Bhavya K. R Research Scholar, Department of CSE, Presidency University, Bengaluru, India
  • S. Pravinth Raja Associate Professor, Department of CSE, Presidency University, Bengaluru, India


Fruit quality prediction, disease identification, CNN, Transfer learning, VGG16


In agricultural farming, defective fruits are the primary cause of global financial disasters. It has an impact on the reliability as well as the quality of the fruits. After harvest, quality inspection necessitates a lot of time and labour-intensive expertise. As a result, saving time and labour during harvest is made possible by automatically detecting fruit quality. With machine learning and image processing techniques, numerous algorithms have been developed to identify and classify fruit quality. A system incorporating Convolutional Neural Networks (CNN) and transfer learning methods have been created to advance the fruit categorization process. Two models are proposed to estimate fruit freshness. One customized CNN architecture is suggested by adjusting the network's parameters to fit the dataset. The second method uses the pre-trained VGG model and the transfer learning approach to determine the fruit's freshness. The suggested models can distinguish fresh and rotten fruit based on the input images. This study used 70 different kinds of fruit, including apples, bananas, oranges, and more. CNN collect features from input images of fruit, and then CNNs with specific categories are used to classify the input images. Because of the model's tailored design, when applied to a Kaggle dataset, the suggested model achieves a 99.39% accuracy on the training data and a 99.99% accuracy on the validation data. The model correctly classified 99.41% of the data. Transfer learning resulted in a 97.65% increase in classification accuracy, a 99.05% increase in training accuracy, and a 99.99% increase in validation accuracy. The results showed that the suggested model could distinguish between fresh and rotten fruit and applicable real-time farming applications.


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Arakeri, M. P. (2016). Computer vision based fruit grading system for quality evaluation of tomatoes in the agriculture industry. Procedia Computer Science, 79, 426-433.

Singh, P. M., Maity, D., Saha, S., & Dhal, N. K. (2022). Seaweed utilization and its economy in Indian agriculture. Materials Today: Proceedings.

Mohapatra, D., Das, N., Mohanty, K. K., & Shresth, J. (2022). Automated Visual Inspecting System for Fruit Quality Estimation Using Deep Learning. In Innovation in Electrical Power Engineering, Communication, and Computing Technology (pp. 379-389). Springer, Singapore.

Mohapatra, D., Das, N., & Mohanty, K. K. (2022). Deep neural network-based fruit identification and grading system for precision agriculture. Proceedings of the Indian National Science Academy, 1-12.

Agriculture and Farming: Production volume of fruits in India from the financial year 2008 to 2021, with an estimate for 2022,

Dhiman, B., Kumar, Y., & Kumar, M. (2022). Fruit quality evaluation using machine learning techniques: review, motivation and future perspectives. Multimedia Tools and Applications, 1-23.

Patil, P. U., Lande, S. B., Nagalkar, V. J., Nikam, S. B., & Wakchaure, G. C. (2021). Grading and sorting technique of dragon fruits using machine learning algorithms. Journal of Agriculture and Food Research, 4, 100118.

Bhargava, A., & Bansal, A. (2020). Automatic detection and grading of multiple fruits by machine learning. Food Analytical Methods, 13(3), 751-761.

Dubey, S. R., & Jalal, A. S. (2016). Apple disease classification using colour, texture and shape features from images. Signal, Image and Video Processing, 10(5), 819-826.

Moallem, P., Serajoddin, A., & Pourghassem, H. (2017). Computer vision-based apple grading for golden delicious apples based on surface features. Information processing in agriculture, 4(1), 33-40.

Mazen, F., & Nashat, A. A. (2019). Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering, 44(8), 6901-6910.

Pande, A., Munot, M., Sreeemathy, R., & Bakare, R. V. (2019, March). An efficient approach to fruit classification and grading using deep convolutional neural network. In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.

Tian, Y., Yang, G., Wang, Z., Li, E., & Liang, Z. (2019). Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense. Journal of Sensors, 2019.

Hu, Z., Tang, J., Zhang, P., & Jiang, J. (2020). Deep learning for the identification of bruised apples by fusing 3D deep features for apple grading systems. Mechanical Systems and Signal Processing, 145, 106922.

Ucat, R. C., & Cruz, J. C. D. (2019, August). Postharvest grading classification of cavendish banana using deep learning and tensorflow. In 2019 International symposium on multimedia and communication technology (ISMAC) (pp. 1-6). IEEE.

Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244-250.

Sudhakara, M., Ghamya, K., Karthik, S. A., Yamini, G., & Mahalakshmi, V. (2022). A Statistical Analysis of Fruit and Vegetables Quality Detection and Disease Classification for Smart Farming. JOURNAL OF ALGEBRAIC STATISTICS, 13(2), 1426-1438.

Khojastehnazhand, M., Omid, M., & Tabatabaeefar, A. (2010). Development of a lemon sorting system based on color and size. African Journal of Plant Science, 4(4), 122-127.

Razak¹, T. R. B., Othman, M. B., bin Abu Bakar, M. N., bt Ahmad, K. A., & Mansor, A. R. (2012). Mango grading by using fuzzy image analysis. In International Conference on Agricultural, Environment and Biological Sciences (ICAEBS'2012) May 26-27, 2012 Phuket.

Zheng, H., & Lu, H. (2012). A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.). Computers and Electronics in Agriculture, 83, 47-51.

Kanade, A., & Shaligram, A. (2015), March). Development of machine vision based system for classification of Guava fruits on the basis of CIE1931 chromaticity coordinates. In 2015 2nd international symposium on physics and technology of sensors (ISPTS) (pp. 177-180). IEEE.

Sahu, D., & Dewangan, C. (2017). Identification and classification of mango fruits using image processing. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 2(2), 203-210.

Maeda, H., Akagi, T., & Tao, R. (2018). Quantitative characterization of fruit shape and its differentiation pattern in diverse persimmon (Diospyros kaki) cultivars. Scientia Horticulturae, 228, 41-48.

Mishra, P., Rutledge, D. N., Roger, J. M., Wali, K., & Khan, H. A. (2021). Chemometric pre-processing can negatively affect the performance of near-infrared spectroscopy models for fruit quality prediction. Talanta, 229, 122303.

Mishra, P., Roger, J. M., Marini, F., Biancolillo, A., & Rutledge, D. N. (2021). FRUITNIR-GUI: A graphical user interface for correcting external influences related to fruit quality prediction in multi-batch near-infrared experiments. Postharvest Biology and Technology, 175, 111414

Fathizadeh, Z., Aboonajmi, M., & Hassan-Beygi, S. R. (2021). Nondestructive methods for determining the firmness of apple fruit flesh. Information Processing in Agriculture.

Minas, I. S., Blanco-Cipollone, F., & Sterle, D. (2021). Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy. Food Chemistry, 335, 127626

Gill, H. S., & Khehra, B. S. (2022). Fruit image classification using deep learning.

Wieme, J., Mollazade, K., Malounas, I., Zude-Sasse, M., Zhao, M., Gowen, A., ... & Van Beek, J. (2022). Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosystems Engineering, 222, 156-176.

Melesse, T. Y., Bollo, M., Di Pasquale, V., Centro, F., & Riemma, S. (2022). Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution. Procedia Computer Science, 200, 13-20.

Darapaneni, N., Tanndalam, A., Gupta, M., Taneja, N., Purushothaman, P., Eswar, S., ... & Arichandrapandian, T. (2022). Banana Sub-Family Classification and Quality Prediction using Computer Vision. arXiv preprint arXiv:2204.02581.

Sun, H., Huang, X., Chen, T., Zhou, P., Huang, X., Jin, W., ... & Gao, Z. (2022). Fruit quality prediction based on soil mineral element content in peach orchard. Food Science & Nutrition.

Mohammed, M., Munir, M., & Aljabr, A. (2022). Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models. Foods, 11(11), 1666




How to Cite

Bhavya K. R, & S. Pravinth Raja. (2023). Fruit Quality Prediction using Deep Learning Strategies for Agriculture. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 301–310. Retrieved from



Research Article