Evaluation of convolutional neural network models' performance for estimating mango crop yield

Abstract

In agriculture, crop yield estimation is essential; producers, industrialists, and consumers all benefit from knowing the early yield. Manual mango counting typically involves the utilization of human labor. Experts visually examine each sample to completethe process, which is time-consuming, difficult, and lacks precision. For commercial mango production to produce high-quality fruits from the orchard to the consumer, a quick, non-destructive, and accurate variety classification is required. Because of its effectiveness in computer vision, a convolutional neural network—one of the deep learning techniques—was chosen for this investigation. For yield prediction, a total of eight popular mango cultivars were utilized. A comparison with previously trained models was used to assess the proposed model. The performance of the classifiers was evaluated using evaluation metrics such as accuracy, loss, area under the receiver operating characteristic curve score, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen’s Kappa performance measure. In terms of performance evaluation criteria, it was found that the proposed approach outperformed the pre-trained models. The suggested model achieved 98.85% accuracy in the test set, which had 800 images. This outcome demonstrates the tangible applicability of the proposed methodology for mango crop estimation.

Keywords

Convolutional Neural Networks / Deep Learning / Early Detection / Machine Learning

References

[1] Ali H., Lali M.I., Nawaz M.Z., Sharif M., & Saleem B.A. (2017). Symptom-based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92-104.

[2] Arsenovic M., Karanovic M., Sladojevic S., Anderla A., & Stefanović D. (2019). Solving current limitations of deep learning-based approaches for plant disease detection. Symmetry, 11, 939.

[3] Atila Ü., Uçar M., Akyol K., & Uçar E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, 101182.

[4] Bedi P., & Gole P. (2021). Plant disease detection using a hybrid model based on convolutional autoencoder and convolutional neural network. Artificial Intelligence in Agriculture, 5, 90-101.

[5] Brahimi M., Mahmoudi S., Boukhalfa K., & Moussaoui A. (2019). Deep interpretable architecture for plant diseases classification. 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 111-116.

[6] Chollet F. (2016). Xception: Deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1800-1807.

[7] Dhingra S., & Bansal P. (2020). Employing divergent machine learning classifiers to upgrade the preciseness of image retrieval systems. Cybernetics and Information Technologies, 20, 75-85.

[8] Fine T.L., Lauritzen S.L., Jordan M., Lawless J., & Nair V. (1999). Feedforward neural network methodology. Springer-Verlag, Berlin, Heidelberg.

[9] Fuentes A., Yoon S., Kim S. C., & Park D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022.

[10] Grinblat G.L., Uzal L.C., Larese M.G., & Granitto P.M. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418-424.

[11] Gyires-Tóth B.P., Osváth M., Papp D., & Szűcs G. (2019). Deep learning for plant classification and content-based image retrieval. Cybernetics and Information Technologies, 19(1), 88-100.

[12] He K., Zhang X., Ren S., & Sun J. (2015). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.

[13] Heredia I., Iglesias L.L., Vykozlov V., & Orviz P. (2019). Plants classification engine. DIGITAL.CSIC.

[14] Huang G., Liu Z., & Weinberger K.Q. (2016). Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261-2269.

[15] Khan A., Sohail A., Zahoora U., & Qureshi A.S. (2019). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53, 5455-5516.

[16] Krizhevsky A., Sutskever I., & Hinton G.E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84-90.

[17] Mohanty S.P., Hughes D.P., & Salathé M. (2016). Using deep learning for image-based plant disease detection. Computers and Electronics in Agriculture, 7, 1419.

[18] Neethi M.V., & Raviraj P. (2024). Fast region-based convolutional neural network ResNet-50 model for on-tree mango fruit yield estimation. Indonesian Journal of Electrical Engineering and Computer Science, 33, 1084.

[19] Sandler M., Howard A.G., Zhu M., Zhmoginov A., & Chen L. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510-4520.

[20] Saleem M.H., Potgieter J., & Arif K.M. (2019). Plant disease detection and classification by deep learning. Plants, 8.

[21] Shi Y., Huang W., Luo J., Huang L., & Zhou X. (2017). Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Computers and Electronics in Agriculture, 141, 171-180.

[22] Simonyan K., & Zisserman A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.

[23] Sladojevic S., Arsenovic M., Anderla A., Culibrk D., & Stefanovic D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 6, 1-11.

[24] Szegedy C., Vanhoucke V., Ioffe S., Shlens J., & Wojna Z. (2015). Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818-2826.

[25] Xiang Q., Wang X., Li R., Zhang G., Lai J., & Hu Q. (2019). Fruit image classification based on MobileNetV2 with transfer learning technique. Proceedings of the 3rd International Conferencen Computer Science and Application Engineering.

[26] Yang K., Qinami K., Fei-Fei L., Deng J., & Russakovsky O. (2020). Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the ImageNet hierarchy. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT '20)*, 547-558.

[27] Yang J., Sun J., Ren Y., Li S., Ding S., & Hu J. (2023). GACP: Graph neural networks with ARMA filters and a parallel CNN for hyperspectral image classification. International Journal of Digital Earth, 16(1), 1770-1800.

[28] Yang X., & Tingwei G. (2017). Machine learning in plant disease research. European Journal of Biomedical Research, 6-9.

[29] Zhang S., Wu X., You Z., & Zhang L. (2017). Leaf image-based cucumber disease recognition using sparse representation classification. Computers and Electronics in Agriculture, 134, 135-141.

[30] Zhang X., Han L., Dong Y., Shi Y., Huang W., Han L., González-Moreno P., Ma H., Ye H., & Sobeih T. (2019). A Zhou, D.X. (2020). Theory of deep convolutional neural networks: Downsampling. Neural Networks, 124, 319-327.

[31] deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing, 11(13), 1554.

[32] Zhou Y., & Diamos G. (2018). Neural architect: A multi-objective neural architecture search with performance prediction. arXiv preprint arXiv:1804.09081.