One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) and Detectron2, and adapts them to lightweight formats—TensorFlow Lite (TFLite) and Open Neural Network Exchange (ONNX)—for resource-constrained devices. Our method leverages two datasets: a comprehensive one and a smaller sample for comparison purposes. With this setup, the authors aimed at using these two datasets to evaluate the performance of the computer vision models and subsequently convert the best-performing models into TFLite and ONNX formats, facilitating their deployment on edge devices. The results are promising. Even in the worst-case scenario, where the ONNX model with the reduced dataset was compared to the YOLOv9-gelan model with the full dataset, the precision reached 87.3%, and the accuracy achieved was 95.0%.

Studies on the development of the root system can provide important information about responses to different management strategies, such as the use of lower quality water, also evaluating the interaction between plants and the physical properties of the soil. This study tested the hypothesis that irrigation with treated sewage effluent (TSE) supplies the water needs of sugarcane plants, increasing root growth and improving the physical properties of the soil. We evaluated the effects of subsurface dripping with TSE or surface reservoir water (SRW) on the root development of first ratoon cane (Saccharum officinarum L.) and the physical properties of dystrophic red latosol. Irrigation treatments were applied at 20 and 40 cm and soil properties were evaluated at soil depth layers of 0–20, 20–40, 40–60, and 60–80 cm. We verified that under irrigation with TSE and SRW, shallower soil layers present better porosity, soil aggregation, and aggregate stability conditions, parameters that improve the root system development and plant growth. On the other hand, deeper soil layers have lower macroporosity and higher total clay volume, indicating the possibility of compaction and greater limitations for sugarcane root growth. These results are important for understanding soil quality and provide significant information for agricultural management and for the implementation of sustainable soil conservation practices. This study shows the efficiency of TSE as an alternative water source for sugarcane crops.