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The management of global food security is one of the major issues of concern to the international community today. Ensuring the stability of food sources and preventing crop pests and diseases are crucial in maintaining social stability and promoting economic development. In modern agriculture, computer vision has emerged as a tool to aid in pest and disease prevention. For instance, when calculating the overall fruit yield of fruit trees and identifying and categorising pests and diseases, traditional neural networks tend to preserve duplicate data during image prediction. Traditional neural networks store unnecessary information when predicting images, leading to more classification calculations and thus higher computing costs. By utilising the concept of deep compressed perception, classification, and other operations can be carried out on compressed data. In this paper, combining compressed sensing theory and traditional neural network techniques, a novel deep compressed sensing network model called CSLSNet is proposed. The model utilizes a parallel convolution and residual structure comprising of convolution, the LR module, and the LSR module. The model directly categorizes images in the compressed domain, leading to decreased computation and a reduction in the number of model parameters. By comparing experiments using different SR (sampling rates) and traditional image compression methods alongside existing network models, this paper demonstrates that our model achieves higher classification accuracy under the same experimental conditions. Also, in fewer training cycles, the loss trend of the proposed model rapidly converges, and the loss curve becomes smoother. The results show that, at a sampling rate of 0.5, our model, CSLSNet, achieves an accuracy of 90.08%. In contrast, other networks involved in the comparison, such as CSBNet and AlexNet, achieve only 84.81% and 86.5%, respectively. [ABSTRACT FROM AUTHOR]