Search Results (5)

Spraying systems to protect crops against pests are still necessary to maintain food production at the rates demanded by the current population. However, today, it is crucial to use precision agriculture to reduce the negative effects of pesticides and other agrochemicals such as fungicides. In particular, pressure fluctuations related to transient states when using pulse-width-modulated nozzles (PMW) have been reported to decrease the accuracy of preset flow rates in air-assisted orchard sprayers. The objective of this paper is to analyze the vibrations induced in the spraying system of a vineyard blast sprayer controlled by pulse-width-modulated nozzles, considering the instantaneous duty cycle (DC) as the control variable. An air-assisted vineyard sprayer was modified to host 24 solenoid shutoff valves with hollow disc–cone nozzles. A triaxial accelerometer was mounted to track the effect of duty cycle (20%, 30%, 50%, and 70%). In addition to accelerations, high-speed images were recorded, and the pressure according to time and the flow were estimated. The hydraulic system of the sprayer, when controlled in real time by the PWM solenoids, created pulsating impacts at the nozzle level with the same frequency of 10 Hz of the PMW system. The impact effect was significantly higher for low duty cycles under 40% DC. In addition, to demonstrate the inaccuracy of opening and closing the valves at a precisely specified time, this study also confirmed the divergence between the theoretical duty cycles commanded by the sprayer’s control unit and the actual ones measured in real time. The results of the analysis showed the difficulty of opening and closing the valves with precision to obtain accurate duty cycles in the practical implementation of smart sprayers and the importance of understanding the vibration effects of pulses in arrangements of multiple PWM nozzles working simultaneously. Full article

Precision farming is the newest agricultural approach in countries with highly mechanized field operations, and the role of unmanned ground vehicles (UGVs) in smart farming is becoming increasingly prominent. This work aimed to evaluate the stability of the DEDALO UGV developed by the University of BOLOGNA for precision orchard and vineyard management. The driving part of the machine is somewhat peculiar; it moves autonomously in the field combined with a tank to store water and pesticide mixture for crop protection, with an additional structure to carry agricultural implements. The study aimed to evaluate the stability of the agricultural unladen UGV, and mulcher and sprayer mounted configurations. In the case of the sprayer, the stability behavior was evaluated with an empty and full tank. The machine, in terms of stability, was studied both laterally and longitudinally. A theoretical model was developed based on the upstream side forces measured during experimental tipping tests. The results of the experimental data were compared with the theoretical predicted results to validate the model. In the lateral test, the average value of the limit stability angle was 48 degrees, while in the longitudinal test, it was 49 degrees. The results of the model were statistically correlative (R² > 95) and denoted that the most stable condition occurred in the case of the UGV fitted with the mulcher in the longitudinal tipping position (56 degrees), while the most unstable condition was the case of the unladen UGV in the longitudinal tipping position (40 degrees). Although the stability problem is not directly connected with the operator, as these machines do not require a driver, the lack of stability can lead to the UGV overturning with consequent risks for the surrounding environment and damage to the UGV body. Full article

Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants. Full article

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision. Full article

Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated. Full article