Research on sustainable computing in agriculture has a great potential as an effective way to solve most agricultural technology bottlenecks, save resource costs, and drive sustainable agricultural development. This paper provides a systematic introduction to the data collection, data mining and evaluation, classification and application of sustainable algorithms involved in the field of sustainable computing in agriculture. At the same time, the paper provides an insightful discussion on its challenges and future trends. The purpose of this work is to help researchers review the current status and pressing issues of sustainable algorithms in agriculture, and to provide a referenceable direction for future research development.

High precision navigation along specific paths is required for plant protection operations in dwarf and densely planted jujube orchards in southern Xinjiang. This study proposes a robotic path planning and navigation method for dense planting of red jujube orchards based on the improved A* and dynamic window approach (DWA) algorithms using Laser Radar to build maps. First, kinematic and physical robot simulation models are established; a map of the densely planted jujube orchard is constructed using Laser Radar. The robot’s position on the constructed map is described using an adaptive Monte Carlo positioning algorithm. Second, a combination of the improved A* and DWA algorithms is used to implement global and real-time local path planning; an evaluation function is used for path optimisation. The proposed path planning algorithm can accurately determine the robot’s navigation paths, with the average error U, average linear path displacement error, and L-shaped navigation being 2.69, 2.47, and 2.68 cm, respectively. A comparison experiment is set up in the specific path navigation section. The experimental results show that the improved fusion algorithm reduces the average navigation positioning deviation by 0.91cm and 0.54 cm when navigating L and U-shaped specific paths. The improved fusion algorithm is superior to the traditional fusion algorithm in navigation accuracy and navigation stability. It can improve the navigation accuracy of the dense planting jujube garden and provide a reference method for the navigation of the plant protection operation in the densely planted jujube orchards.

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

With the continuous development of artificial intelligence, more and more human–computer interaction (HCI) applications have begun to appear in the field of education. This article investigates the relationship between the HCI and cognitive load and academic emotion by comparing the effect of classroom discussion in the smart classroom with that of traditional classroom discussion and classroom discussion that focuses too much on learning interest. By using classroom test questionnaire and interview questionnaire, the experiment counted the test scores and questionnaire satisfaction of participants in different classroom discussions, and obtained the learning effect and the emotional state and satisfaction degree of learners under different classroom discussions. It is concluded that the artificial intelligence in the HCI of smart classroom should always take the amount of cognitive load of learners as the core, improve the academic emotion of learners as the auxiliary, reduce the cognitive load of learners as much as possible, and improve the academic emotion of learners within a reasonable range, so as to improve the learning effect. It also provides methods and suggestions for how to reduce learners’ cognitive load and how to reasonably improve their academic emotions in smart classroom.

As a crucial economic crop, the health status of cotton directly impacts farmers' income and the national economy. Therefore, timely and accurate detection and identification of cotton diseases and pests are of significant importance, aiding in reducing the adverse effects of diseases and pests on cotton yield and quality. Existing research struggles to address the balance between resource consumption and detection accuracy in cotton disease and pest detection. Moreover, diseases and pests often occur beneath the canopy, and the orthorectification of drone imagery may result in insufficient feature information and prolonged processing time, among other issues. To address the aforementioned issues, this paper proposes a precise detection method for cotton Verticillium wilt based on unmanned aerial vehicle multi-angle remote sensing guided by a satellite time series monitoring model. Specifically, first, combining Sentinel-1 microwave and Sentinel-2 optical time series images, we constructed a cotton Verticillium wilt monitoring model based on extreme gradient boosting algorithm to identify areas affected by the disease invasion. Subsequently, after identifying the blocks affected by the disease, we collected multi-spectral remote sensing data captured from multiple angles by unmanned aerial vehicles and compared different combinations of vegetation indices and bands. Finally, we constructed a precise classification model for cotton Verticillium wilt based on support vector machine radial basis function classification method. Experimental results indicate that the joint microwave and optical time-series monitoring model achieved OA of 81.73% and Kappa coefficient of 0.63, meeting the monitoring requirements of the first stage. Based on the SVM with RBF and the optimal band combination, the OA value of the comprehensive image captured at -58° angle reached 96.74%, with Kappa coefficient of 0.93, meeting the requirements of precise classification detection in the second stage.