Tractor-mounted, GPS-based spot fumigation system manages Prunus replant disease

Our research goal was to use recent advances in global positioning system (GPS) and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in a small target treatment zone in and around each tree replanting site) to control Prunus replant disease (PRD). We developed and confirmed the function of (1) GPS-based software that can be used on cleared orchard land to flexibly plan and map all of an orchard's future tree sites and associated spot fumigation treatment zones and 2) a tractor-based GPS-controlled spot fumigation system to quickly and safely treat the targeted tree site treatment zones. In trials in two almond orchards and one peach orchard, our evaluations of the composite mapping and application system, which examined spatial accuracy of the spot treatments, delivery rate accuracy of the spot treatments, and tree growth responses to the spot treatments, all indicated that GPS spot fumigation has excellent potential to greatly reduce fumigant usage while adequately managing the PRD complex

Development of an automatic tracking system to determine field efficiency of agricultural machines

Field efficiency of machines tells how efficient the farm machines are operating in the field. Measuring of the field efficiency used to be a tedious and laborious work which is not worth to collect for further operational optimization. The objective of this study was to develop an automatic system for monitoring the field activities and then evaluation of the field efficiency of farm machines. The system consisted of a microcontroller to collect working data including position, speed heading, and working status of the machine. The system was installed on a farm tractor with plowing disc to test on two fields with the same size, but in different traveling directions, i.e., lengthwise and crosswise. The results showed lengthwise operation yielded a higher field efficiency due to less number of turning at headlands. The proposed system allowed to collect necessary information for detailed efficiency evaluation of farm machines. This technique enables further utilization of the operational information and benefit to use in the optimization of the farm works

Determination of field capacity for the sugarcane harvester using GNSS data

Abstract Harvesting is an important activity in the sugar production industry. Due to the labor shortage and time limitation during harvesting season, farmers have adopted cane harvesters to substitute the farm workers in this restless period. Cane harvesters are huge and expensive machines with high field capacity. Because of inappropriate working conditions in Thailand, the actual field capacity is much lower than that in its specification. The objective of this research is to study the factors affecting the field capacity of the sugarcane harvester. A GNSS logging system was used to record the machine’s position and traveling speed during operation. Crop yield for each field was also collected. Field dimension and other working parameters such as working time and the number of turns were derived from the GNSS data. A field capacity prediction model was developed. The study shows that the optimal working speed, crop yield, and the number of turns per field area were significant factors to predict the harvester’s field capacity. The coefficient of determination (R2 value) of the model was 0.625. It was suggested to include more machine and field variation for further robust model development and uses in the optimization of field operation performance.</jats:p

Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques

Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis&ndash;SWNIR) spectroscopy (400&ndash;1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis&ndash;SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications

Design of a laboratory-scale sugarcane weighing system

Abstract Recently, sugarcane harvesters have been increasingly used in sugarcane harvesting. Loading trucks were traveling along the harvesters to collect the harvested cane billets. Since cane harvesters are expensive machines, there is an idea of collaborative farming by combining multiple fields from different owners to reduce operating costs and time. However, it is difficult to fairly classify yields from different fields. Site-specific yield monitoring system is not common in typical harvesters. Farmers only know the weight on each truck without its collecting location when selling the sugarcane to the factory. This research was the feasibility study to develop a hydraulic weighing system in laboratory scale for further applying to the side-tipping loading trucks. A low-cost hydraulic weighing system was fabricated. A microcontroller was used to read signals from pressure and gyroscopic sensors and then to calculate the applied load. Accuracy and precision of the system were examined. The coefficient of determination (R2) of the relationship between the actual and determined loads was 0.978. The standard error of prediction (SEP) of the system was 2.348 kg. The results show that there was feasibility to apply the system on farm scale; however, further study with a larger scale should be conducted.</jats:p

Development of an automatic tracking system to determine field efficiency of agricultural machines

Field efficiency of machines tells how efficient the farm machines are operating in the field. Measuring of the field efficiency used to be a tedious and laborious work which is not worth to collect for further operational optimization. The objective of this study was to develop an automatic system for monitoring the field activities and then evaluation of the field efficiency of farm machines. The system consisted of a microcontroller to collect working data including position, speed heading, and working status of the machine. The system was installed on a farm tractor with plowing disc to test on two fields with the same size, but in different traveling directions, i.e., lengthwise and crosswise. The results showed lengthwise operation yielded a higher field efficiency due to less number of turning at headlands. The proposed system allowed to collect necessary information for detailed efficiency evaluation of farm machines. This technique enables further utilization of the operational information and benefit to use in the optimization of the farm works