## 1 Introduction  
Deep learning (DL) is playing a crucial role in precision agriculture to improve the yield of the farm [1, 2, 3]. However, applying deep learning in agriculture involves the acquisition and processing of a large amount of data related to crop. These data are essentially collected, based on wireless sensors (aboveground or underground), drones, satellites, robots, etc. Due to the huge amount of parameters, DL models are usually inefficient on low-cost devices with limited resources [4]. As a result, they are usually deployed on remote servers. Remote processing raises two issues: the latency of the network, and the bandwidth saving. Farm automation solutions such as automatic harvesting [5], fruit counting [6], etc., require a very short response time that is not easily provided by remote computation. The closer the processing unit is in the farm, the smaller is the latency. Apart from latency, most weakly industrialized economies experience low Internet penetration rates, especially in rural areas where agricultural activities are mainly performed. Many rural areas that do have connectivity have low bandwidths that only allow limited data traffic [7, 8]. This can also increase the response time since DL models process a huge amount of data. A recent approach to overcome those limitations is infield deployment that can provide more efficient real-time reaction, critical for crop monitoring. Infield deployment generally relies on edge computing [9] that allows incoming data to be analyzed close to the source with a minimal footprint.  
Edge computing adapts well to limited computing performance and it is suitable for infield inference, which enables real-time reaction and facilitates the sending of refined data or results over narrowband networks. Since devices used in agricultural field are resource-limited, using infield DL models is considered to be a significant challenge.  
Few works [10; 11] on DL in agriculture take into account the limitation of resources. Authors designed a model compression technique based on separable convolution and singular value decomposition. They applied their technique on very deep convolutional neural networks (CNNs) for plant image segmentation, with the aim of deploying the new model infield.  
In contrast to the works of [10; 11], which focus on segmentation (background/flower), this paper focuses on classification, which is a more common task in the application of DL in agriculture. This work proposes a combination of two methods namely pruning and quantization, which are applicable to any type of neural network and allows to obtain higher compression ratio. Three DL models (LeNet5, VGG16, and AlexNet) are compressed and applied on two datasets used in agriculture: V2 Plant Seedlings Dataset [12] and Flavia database [13].  
The rest of the paper is organized as follows: Section 2 briefly presents common DL models as well as the constraints of deploying such models on low-cost and limited-resource devices. Section 3 describes of up-to-date techniques in model compression. The proposed model approach and experimental setup are discussed, respectively, in Sections 4 and 5. The evaluation on plant disease datasets is done in Section 6, and Section 7 concludes the present work.