Advanced Non-Chemical and Close to Plant Weed Control system for Organic Agriculture

Use of chemical has been reduced in agriculture for controlling weeds emergence. The use of alternative systems, such as cultural practices (mulching, flame, intercropping etc.) and mechanical system (hoe, tine etc.) has been introduced by various researchers. Automation technique based on sensors controlled system has enhanced the efficiency of the mechanical system for weed control. Mostly, low cost image acquisition sensors and optical sensor to detect the plant ensuring swift operation of vehicles close the crop plants to remove competitive weeds. The available system need to be evaluated to get best possible system for close to plant (CTP) weed removal. In the study various non-chemical weed control measures has been explored and 30 mechanical tools for CTP were evaluated. High precision tillage solutions and thermal weed control by pulsed lasers for eradication of stem or main shoot were found to be the most promising weed control concepts for CTP operation

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

The profitability of precision spraying on specialty crops: a technical–economic analysis of protection equipment at increasing technological levels

A technical–economic analysis was conducted on three different technological levels of spraying equipment for specialty crops, based on the results on precision spraying technologies reported in scientific literature. The application scenarios referred to general protection protocols against fungal diseases adopted in vineyards and apple orchards in Central-Southern Europe. The analysis evaluated the total costs of protection treatments (equipment + pesticide costs), comparing the use of conventional air-blast sprayers (referred to as L0), of on–off switching sprayers (L1), and of canopy-optimised distribution sprayers (L2). Pesticide savings from 10 to 35% were associated with equipment L1 and L2, as compared to L0. Within the assumptions made, on grapevines, the conventional sprayer L0 resulted in the most profitable option for vineyard areas smaller than 10 ha; from 10 ha to approximately 100 ha, L1 was the best option, while above 100 ha, the more advanced equipment L2 resulted in the best choice. On apple orchards, L0 was the best option for areas smaller than 17 ha. Above this value, L1 was more profitable, while L2 never proved advantageous. Finally, in a speculation on possible prospectives of precision spraying on specialty crops, the introduction of an autonomous robotic platform able to selectively target the pesticide on diseased areas was hypothesised. The analysis indicated that the purchase price that would make the robotic platform profitable, thanks to the assumed pesticide and labour savings over conventional sprayers, was unrealistically lower than current industrial cost. This study showed that, in current conditions, profitability cannot be the only driver for possible adoption of intelligent robotic platforms for precision spraying on specialty crops, while on–off and canopy-optimised technologies can be profitable over conventional spraying in specific conditions

Ammendatud freesturbaväljadel kasvatatava ahtalehise mustika masinviljelustehnoloogia

A Thesis for applying for the degree of Doctor of Philosophy in Engineering Sciences.Blueberry cultivation on milled peat fields is not particularly common in Estonia, while also not being very profitable. The basis for the development of blueberry cultivation is the mechanization and automation of production. This consists, on the one hand, in the development of machines and technical equipment with suitable productivity and, on the other hand, in reducing the operating costs of the machines. This doctoral thesis is largely based on six original publications and two intellectual properties. The aims of the thesis were to describe the technological peculiarities of a blueberry orchard planted on milled peat fields, to collect basic data for the development of a machinery which allows to reduce the importance of manual labour and to replace it with machinery to reduce the unit cost involved in technological operations and, thereby, to reduce manufacturing costs. In order to compile the initial task of designing a mechanized blueberry harvesting technology, the relationship between the various elements which are involved in blueberry cultivation (berry-plant-field-machine), all of which have been described, methodology has been developed to determine the physical properties of the blueberry plant. A methodology has been developed for determining the design and kinematic parameters of a motoblock-type blueberry harvester and for selecting the material of the harvester, the duration of the vernalisation period in Estonia was also determined. The patents that have been issued in the development of blueberry cultivation technology show that novel solutions have been elaborated. The studies that have been carried out and the solutions which have been developed could help in and become a prerequisite for the development of new equipment which will serve to foster the establishment of new blueberry plantations, first and foremost on milled peat fields, but also in terms of increasing profitability levels and reducing the ecological footprint in already established blueberry plantations.Kultuurmustikate kasvupind ja kogutoodang on maailmas oluliselt suurenenud. ÜRO Toidu- ja Põllumajandusorganisatsiooni andmetel kasvatati 2018. aastal kultuurmustikaid 113 000 hektaril ja kogutoodang ulatus 666 000 tonnini. Eestis moodustab mustikate hinnanguline kasvupind kuni 80 hektarit. Kultuurmustikate kasvatus ei ole Eestis praegu eriti tulus, kuna mustikaistandused on väikesed ja tööd tehakse peamiselt käsitsi. Mustikaviljeluse arenguks on vaja tootmist mehhaniseerimida ja automatiseerida. See seisneb sobiva tootlikkusega masinate ja tehniliste vahendite arendamises ning masinate kasutuskulude vähenemises. Väitekiri on koostatud kuue artikli ja kahe patendi põhjal. Uurimuses kirjeldati ammendatud freesturbaväljadele rajatud mustikaistanduse tehnoloogilisi iseärasusi. Uuringute käigus koguti lähteandmeid masinalise mustikaviljelusviisi väljatöötamiseks ning masinate ja tehniliste vahendite arendamiseks. Uurimistöö eesmärk oli suurendada masinate ja seadmete tootlikkust ja vähendada tööjõumahukust mustikaviljeluses tervikuna. Töö käigus kirjeldati mustikaviljelussüsteemi elementide (mari-taim-põld-masin) seoseid, määrati viljelussüsteemi elementide (mari, vars) mehaanikalised parameetrid, kavandati mustikakombaini uudse lahendusega korjeorgan, määrati vernalisatsiooniperioodi pikkus Eestis ja loodi tehnilised lahendused mustikakombainile, portatiivsele täppisväeturile, portatiivsele taimekaitsevahendi laoturile ja marjasorteerile tehnoloogiliste masinaliste tööoperatsioonide läbiviimiseks. Mustika masinviljelustehnoloogia arendusele väljastatud patendid annavad tunnistust uudsetest tehniliste lahendustest, mis võivad olla abiks ja eelduseks uute seadmete loomisel, mis aitavad kaasa uute mustikaistanduste loomisele eelkõige ammendunud freesturbaväljadel ning kasumlikkuse suurendamiseks ja ökoloogilise jalajälje vähendamiseks juba rajatud mustikaistandustes.Publication of this thesis is supported by Estonian University of Life Sciences and the Doctoral School of Energy and Geotechnology III and Estonian University of Life Sciences ASTRA project „Value-chain based bioeconomy“

Review on Automatic Variable-Rate Spraying Systems Based on Orchard Canopy Characterization

Pesticide consumption and environmental pollution in orchards can be greatly decreased by combining variable-rate spray treatments with proportional control systems. Nowadays, farmers can use variable-rate canopy spraying to apply weed killers only where they are required which provides environmental friendly and cost-effective crop protection chemicals. Moreover, restricting the use of pesticides as Plant Protection Products (PPP) while maintaining appropriate canopy deposition is a serious challenge. Additionally, automatic sprayers that adjust their application rates to the size and shape of orchard plantations has indicated a significant potential for reducing the use of pesticides. For the automatic spraying, the existing research used an Artificial Intelligence and Machine Learning. Also, spraying efficiency can be increased by lowering spray losses from ground deposition and off-target drift. Therefore, this study involves a thorough examination of the existing variable-rate spraying techniques in orchards. In addition to providing examples of their predictions and briefly addressing the influences on spraying parameters, it also presents various alternatives to avoiding pesticide overuse and explores their advantages and disadvantages

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Mid to Late Season Weed Detection in Soybean Production Fields Using Unmanned Aerial Vehicle and Machine Learning

Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were compared with the Mobilenet v2 based convolutional neural network (CNN) model for their classification performance. The results showed that the CNN model had the best classification performance for individual patches. Two different image slicing approaches – patches with and without overlap were tested, and it was found that slicing with overlap leads to improved weed detection but with higher inference time. For the object detection approach, two models with different network architectures, namely Faster RCNN and SSD were evaluated and compared. It was found that Faster RCNN had better overall weed detection performance than the SSD with similar inference time. Also, it was found that Faster RCNN had better detection performance and shorter inference time compared to the patch-based CNN with overlapping image slicing. The influence of spatial resolution on weed detection accuracy was investigated by simulating the UAV imagery captured at different altitudes. It was found that Faster RCNN achieves similar performance at a lower spatial resolution. The inference time of Faster RCNN was evaluated using a regular laptop. The results showed the potential of on-farm near real-time weed detection in soybean production fields by capturing UAV imagery with lesser overlap and processing them with a pre-trained deep learning model, such as Faster RCNN, in regular laptops and mobile devices. Advisor: Yeyin Sh