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Pineapples are smarter and easier to produce

Currently in Taiwan pineapple is a major fresh fruit for export in terms of amount, thus the control and upgrade of fruit quality is essential to the sustainable development of the industry. However, pineapple industry requires large labor input involved with planting, management, flower forcing, harvest and packing. As labor force aged and dwindling, the introduction of automatic and smart techniques is crucial and primary to the key solution of labor shortage. This project aims to develop monitoring system, establishment of database, digital service platform and strengthen fruit selection in packing house. As considering the wide distribution of farmlands in specific regions and demands of environmental monitoring on field practice, the LoRa transmission is integrated with environment monitoring sensor to form the environmental monitoring system. This instrument sends information through LoRa system to reduce the wireless fee and facilitate the build-up of environmental information. This study also includes a database to link the field leaf nutrient status, fruit metabolites, quality, fruit storage capability, with all the analysis results provided to enhance the precision of field management. Furthermore, based on the analysis of database, the biological index to predict storage capability was established to serve for new alternatives of fruit selection in packing house. To upgrade field management, the iPLANT system, information system for accreditation information collection, was introduced to the industry. The system has integrated with expertise know-how to enhance source trace management and warning on abnormal environmental condition. Based on the analysis of characteristics of fruit scrape by spectrum, a multiple spectrum selection has developed to accelerate the rate of fruit scrape identification in packing house. Furthermore, the fruit weight sorting system was added with counting sensor to automatically recording the fruit number in each grade. The records can will be served as fundamental of database related to the fruit yield and quality from each farmlands. Through the application of smart and digital techniques combining with farmers’ field experiences and experts’ advises, we wish to keep on reducing the farmer’ labor and time loading, and production risks, in the meanwhile also, to promote fruit quality and enhance the competitiveness of pineapple industry.

Title

Introduction

IoT of farmland environment monitoring

In each production area, the weather and soil characteristics different from site to site and in great variance in the same town. In order to monitor the micro climate and soil environment, the LoRa based environental mornitoring sensors were developed.

UAV based field monitoring system

The monitoring of crop status majorly uesd to relies on field observation and was labor consuming. This study aims at using unmanned aerial vehicle (UAV), camera, and edge computing with microcomputer to fulfill real time information capturing. The crop status information e.g. leaf nitrogen concentration, growing stage are analyzed by figure analysis system at the same time the image capturing, transferring to user through picture transmission system on the UAV.

Pineapple automatic counter

This system is for real time counting in pineapple fruit weight grading process, which shows the fruit numbers in each grading and saves the results in PC or cloud drive for futher analysis. The system upgrades the original grading system with smart sensor, as the fruit drop down from the tray and the balace knock back to original state, the sensking signal then sent to the system and counting the number. This system has been designed in easy Easy to disassemble without further modification in the conventional machine.

Smart cultivation technique of pineapple farmlands

The soil fertility, leaf nutrient status, fruit metabolites and fruit storage database of pineapple was established. Fom the analysis of database, the internal browning estimation technique thus is esbablished according to the metablites composition of fruit. This technique provides opprtunity to estimate the storage capacity during packing house to avoid poor quality fruit arrived in market. Furthermore, the key leaf nutrients affecting fruit quality were identified as reference to develop the field management technique.

Spectral based non-destrutive selection techniquen

The non-destrutive selection system consists multi-spectral imaging system and AI technique, which is supplied to identify internal browning from chilling and fruit bruist in the packing house. The internal browning model was established and validated by different batch of harvested fruit, and the accuracy is achive 80%. The multispectral image can analysis the bruist fruit since the first day harrested as the fruit is difficult to identified by eyes.

Accreditation support system

The system is develped to improve smart farming techniques on the pineapple industry. The spatial and temporal information of pineapple during forcing to harvest are established in the information system to integrate the location, date and management events. The peak of fruit supplied in each area could be analyzed by collecting the information from representative farmers in each area. The strategies can be applied before production season to alleviate the price volatility during the major production season.
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