In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.
Authors have received research and financial support from the Ministry of Electronics and Information Technology (MeitY), Delhi, Government of India under Administrative Approval Number: 26(6)2019-ESDA.
Alam, A., Chauhan, A., Khan, M.T. and Jaffery, Z.A. (2022), "Drone-Based Crop Product Quality Monitoring System: An Application of Smart Agriculture", Mor, R.S., Kumar, D. and Singh, A. (Ed.) Agri-Food 4.0 (Advanced Series in Management, Vol. 27), Emerald Publishing Limited, Bingley, pp. 97-110. https://doi.org/10.1108/S1877-636120220000027007
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