Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system’s detection accuracy.

The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives to herbicides, which are becoming less effective as weed species develop resistance. Advances in deep learning technology have significantly improved the robustness of weed detection tasks. However, deep learning algorithms often require extensive computational resources, typically found in powerful computers that are not suitable for deployment in robotic platforms. Most ground rovers and UAVs utilize embedded computers that are portable but limited in performance. This necessitates research into deep learning models that are computationally lightweight enough to function in embedded computers for real-time applications while still maintaining a base level of detection accuracy. This paper evaluates the weed detection performance of three real-time-capable deep learning models, YOLOv4, EfficientDet, and CenterNet, when run on a deep-learning-enabled embedded computer, an Nvidia Jetson Xavier AGX. We tested the accuracy of the models in detecting 13 different species of weeds and assesses their real-time viability through their inference speeds on an embedded computer compared to a powerful deep learning PC. The results showed that YOLOv4 performed better than the other models, achieving an average inference speed of 80 ms per image and 14 frames per second on a video when run on an imbedded computer, while maintaining a mean average precision of 93.4% at a 50% IoU threshold. Furthermore, recognizing that some real-world applications may require even greater speed, and that the detection program would not be the only task running on the embedded computer, a lightweight version of the YOLOv4 model, YOLOv4-tiny, was tested for improved performance in an embedded computer. YOLOv4-tiny impressively achieved an average inference speed of 24.5 ms per image and 52 frames per second, albeit with a slightly reduced mean average precision of 89% at a 50% IoU threshold, making it an ideal choice for real-time weed detection.

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields.

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop’s growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms.

Herbicides have been the primary weed management practice in agriculture for decades. However, due to their effects on the environment in addition to weeds becoming resistant, alternative approaches to weed control are critical. One approach is using lasers, particularly diode lasers because of their portability, low power demand, and cost effectiveness. In this research, weeds’ response to diode laser treatments was investigated. Three experiments were conducted. The first experiment involved treating two species of weeds with four different laser powers to determine the time it takes to sever the weed stem. The second experiment involved monitoring the status of two species of weeds for a week after treating them with two lasers at constant application times of 1 s, 2 s, and 3 s. The third experiment was a repeat of the second with higher laser powers and shorter treatment times. The results showed diode lasers have a potential to be an effective weed controlling tool. Weed stem diameter, laser power, treatment duration, and distance between laser and weed were all statistically significant in weed mortality, with weed species having no significance. Furthermore, it was found that weed management is possible by exposing the stem of the two weed species between 0.8 and 2.65 mm diameter to a laser beam dosage without necessarily severing it, with 80% effectiveness at 0.5 s treatment time, and 100% effectiveness using a 6.1 W laser for 1.5 s.

In this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap were recorded 2× per day. A total of 120 whitefly images were labeled using labeling software and split into a training and testing dataset, and 18 additional yellow-stick tape images were labeled with false positives to increase the model accuracy from remote whitefly monitors in the field that created false positives due to water beads and reflective light on the tape after rain. The two-shot detection model has two stages: region proposal and then classification of those regions and refinement of the location prediction. Single-shot detection skips the region proposal stage and yields final localization and content prediction at once. Because of this difference, YOLOv4 is faster but less accurate than Faster-RCNN. From the results of our study, it is clear that Faster-RCNN (precision—95.08%, F-1 Score—0.96, recall—98.69%) achieved a higher level of performance than YOLOv4 (precision—71.77%, F-1 score—0.83, recall—73.31%), and will be adopted for further development of the monitoring station.