Pesticide Residue Estimation in Brinjals using 3D Squeeze Excitation-Residual Spectral Network (SERSN) ✱

The detection of pesticide residue in vegetables is becoming evident in modernized agriculture. Our present work used visible/near-infrared (400–1000 nm) Hyperspectral Imaging Systems (HSIs) to detect the pesticide residue levels in Brinjal. This paper proposes a deep learning framework named the 3D Squeeze Excitation-Residual Spectral Network (3D SERSN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral residual blocks consecutively learn discriminative features from abundant spectral signatures and the newly introduced SE block inside the residual block keeps the skip connection branch as clean as possible to make the learning identity easy. The proposed network is evaluated on the developed HSI dataset of Brinjals. Image samples were captured using a hyperspectral camera for Brinjals without pesticide and with pesticides at different concentrations of 0.5ml, 3ml and 6ml in 1 litre of water namely low, medium and high. Two types of classification analysis, two classes (with or without pesticide) and four classes (pure, low, medium and high) were done with a set of machine learning algorithms and using the proposed deep network. The classification analysis was also implemented with seven machine learning algorithms and with a CNN. For the four-class classification problem, the Multi-class support vector machine and the Discriminant analysis Algorithm reported the highest accuracy of 89%; for the two-class classification, the K-nearest neighbour classifier recorded the highest accuracy of 78%. The proposed 3D SERSN gives an accuracy of 94% for the two-class problem and 98% for the four-class problem on the proposed dataset for pesticide residue estimation on brinjals, showing the effectiveness of our suggested network for the stated issue.1