EVALUATING THE PERFORMANCE OF A SEMI-AUTOMATIC APPLE FRUIT DETECTION IN A HIGH-DENSITY ORCHARD SYSTEM USING LOW-COST DIGITAL RGB IMAGING SENSOR

This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina’s Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil’s apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I1 , I2 , I3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I”2 , I”3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I”2 , I”3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed a 0.73 coefficient of determination (R2 ), and fruit recognition accuracy rate showed 0.96 R2 . The proposed approach provides results with great applicability for small holder farms and may support local harvest prediction

Western corn rootworm pyrethroid resistance confirmed by aerial application simulations of commercial insecticides

The western corn rootworm (Diabrotica virgifera virgifera LeConte) (WCR) is a major insect pest of corn (Zea mays L.) in the United States (US) and is highly adaptable to multiple management tactics. A low level of WCR field-evolved resistance to pyrethroid insecticides has been confirmed in the US western Corn Belt by laboratory dose-response bioassays. Further investigation has identified detoxification enzymes as a potential part of the WCR resistance mechanism, which could affect the performance of insecticides that are structurally related to pyrethroids, such as organophosphates. Thus, the responses of pyrethroid-resistant and -susceptible WCR populations to the commonly used pyrethroid bifenthrin and organophosphate dimethoate were compared in active ingredient bioassays. Results revealed a relatively low level of WCR resistance to both active ingredients. Therefore, a simulated aerial application bioassay technique was developed to evaluate how the estimated resistance levels would affect performance of registered rates of formulated products. The simulated aerial application technique confirmed pyrethroid resistance to formulated rates of bifenthrin whereas formulated dimethoate provided optimal control. Results suggest that the relationship between levels of resistance observed in dose-response bioassays and actual efficacy of formulated product needs to be further explored to understand the practical implications of resistance

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe

Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate

Development trend of agricultural drone technology based on patent analysis

In recent years, global agricultural drone technology patent applications have continued to grow rapidly every year. In the global applicant rankings, the top 10 applicants are all Chinese applicants, with Chinese companies and universities far ahead. The patent applied for is mainly about operation management, which is closely related to the application scenarios of agricultural drones. In order to study the development trend of agricultural drone technology, the patent applications in the field of agricultural drone technology after 2009 were analyzed. The characteristics of agricultural drone technology patent activities are revealed from the perspectives of overall trends, geographical distribution, major competitors, and technical composition, and the development trend of agricultural drone technology is revealed from the perspective of patents. The results show that the agricultural drone technology is in the stage of technological development and has a bright future. In the next few years, the number of patent applications and applicants related to agricultural drone technology will continue to maintain a high growth trend. Overseas layout, improving the awareness of patent protection has become the focus. The research results can provide reference for the development of agricultural drone industry

Design and development of an unmanned aerial and ground vehicles for precision pesticide spraying

Günümüzde, bitki hastalıkları tarımsal üretimi etkileyen önemli sorunlardan birisi olarak karşımıza çıkmaktadır. Bitkileri hastalıklardan ve zararlı otların etkilerinden korumak hem tarımda üretimi artırmak hem de tarımın kalitesini yükseltmek için büyük önem taşımaktadır. Tarımsal ürünler, ülkemizde ve dünyada çeşitli ilaçlama yöntemleri kullanılarak korunabilmektedir. Bu yöntemlerin başında gelen ilaçlama yolu ile bitki koruma yöntemi üretimin kalitesini geliştirmek ve rekolteyi artırmak amacıyla yaygın olarak kullanılmaktadır. Ancak bitkilerin korunmasında uygulanan geleneksel ilaçlama yöntemlerinin bitkilere ve toprağa büyük ölçüde zarar verdiği gözlenmektedir. Son yıllarda gelişmiş ülkelerdeki tarımsal uygulamalarda robotların kullanımı hızla artmakta, tarımsal alanlarda özellikle uzaktan algılama ve hassas tarım çalışmalarında bu robotların kullanıldığı görülmektedir. Dahası, tarımsal üretimde yararlanılan fayda-maliyet oranı da dikkate alındığında, günümüzde hassas tarım uygulamalarında robotların kullanılmasının kaçınılmaz hale geldiği anlaşılmaktadır. Günümüz gereksinimleri ve gelişen teknoloji göz önüne alınarak planlanmış olan bu çalışmada, ülkemizde yaygın olarak kullanılan tarımsal mücadele yöntemlerinin maliyetlerini, tarımsal üretimin miktarını ve kalitesini önemli ölçüde etkileyecek geleneksel ilaçlama yöntemlerine alternatif olabilecek bir tarımsal mücadele sistemi geliştirilmiştir. Çalışmada, yakın mesafeden doğrudan hedeflenen bitki üzerine ilaçlama yapılması, ilaçlama sırasında toprağa ve bitkilere verilen zararın en aza indirgenmesi hedeflenmiştir. Bu doğrultuda, özgün tasarım multispektral kamera, ilaçlama ünitesi, Yer Kontrol İstasyonu (YKİ) ve eşgüdümlü çalışabilen İnsansız Hava Aracı (İHA) ile İnsansız Yer Aracından (İYA) oluşan tarımsal mücadele mekanizması tasarlanmış ve geliştirilmiştir. Bu mekanizma, tarımsal ilaçlama uygulamaları için geleneksel yöntemlere kıyasla daha ileri düzey bir alternatif yöntem olarak ortaya çıkmaktadır.TABLE OF CONTENTS ÖZET ................................................................................................................ vii ABSTRACT ....................................................................................................... ix ACKNOWLEDGEMENTS ................................................................................ xi 1 . INTRODUCTION .......................................................................................... 1 2. LITERATURE REVIEW ............................................................................. 6 2.1 Robotics ..................................................................................................... 9 2.2 Unmanned Ground Vehicles ..................................................................... 11 2.3 Unmanned Aerial Vehicles ....................................................................... 11 2.4 Remote Sensing Technology .................................................................... 17 2.4.1 Remote Sensing Platforms ................................................................. 19 2.4.2 Plant Disease Detection ..................................................................... 22 2.4.3 Normalized Difference Vegetation Index ........................................... 27 3 . MATERIAL AND METHOD ....................................................................... 29 3.1 Ground Control Station ............................................................................ 32 3.2 Unmanned Ground Vehicle ...................................................................... 37 3.2.1 Specifications of the UGV ................................................................. 38 3.2.2 The Chassis and Sensor Holder .......................................................... 40 3.2.3 FEM Analysis .................................................................................... 43 3.3 Multispectral Camera for Plant Disease Detection .................................... 44 3.3.1 Spectral Imaging ................................................................................ 46 3.3.2 Multispectral Camera – Spektra TSL128RN ...................................... 47 3.3.3 The hardware of the Device ............................................................... 49 3.3.4 Calibrating Steps of the Device .......................................................... 52 3.3.5 Software for the Device ..................................................................... 56 3.3.6 Measurements using NDVI Devices .................................................. 58 3.4 Unmanned Aerial Vehicle ........................................................................ 62 3.4.1 The Chassis and Arm ......................................................................... 66 3.4.2 FEM Analysis ................................................................................... 69 3.4.3 Modal Analysis ................................................................................. 70 3.4.4 Performance of the Propellers ............................................................ 73 3.4.5 Flight Duration and Maximum Conditions ......................................... 82 3.4.6 Strain Measurement ........................................................................... 84 3.4.7 Other Parts ........................................................................................ 92 3.4.8 Specifications of the UAV ................................................................. 95 3.4.9 Flight Tests ....................................................................................... 96 3.5 Spraying Unit –Sprayer and Tank ............................................................. 99 4 . RESULTS AND DISCUSSION .................................................................. 103 4.1 The UGV ............................................................................................... 103 4.2 The Multispectral Camera ...................................................................... 105 4.3 The UAV ............................................................................................... 115 4.4 The Sprayer............................................................................................ 135 xv 4.5 UGV and Multispectral Camera .............................................................. 138 4.6 Aerial Spraying UAV ............................................................................. 145 5 . CONCLUSIONS......................................................................................... 154 REFERENCES ................................................................................................ 156 RESUME......................................................................................................... 16

Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

In conservation, the use of unmanned aerial vehicles (drones) carrying various sensors and the use of deep learning are increasing, but they are typically used independently of each other. Untapping their large potential requires integrating these tools. We combine drone-borne thermal imaging with artificial intelligence to locate ground-nests of birds on agricultural land. We show, for the first time, that this semi-automated system can identify nests with a high performance. However, local weather, type of arable field and height of the drone can affect performance. The results’ implications are particularly relevant to conservation practitioners working across sectors, such as biodiversity conservation and food production in farmland. Under a rapidly changing world, studies like this can help uncover the potential of technology for conservation and embrace cross-sectoral transformations from the onset; for example, by integrating nest detection within the precision agriculture system that heavily relies on drone-borne sensors.Peer reviewe

Drones, UAVs, and RPAs

While UAVs have been around since World War One, they have been restricted to odd military uses until recently. Now UAVs, or drones, are becoming a more significant part of the modern world. This paper seeks to analyze the technical, domestic, international, and humanitarian ramifications of widespread UAV use as well as offer solutions to the problems inherent with this technology. Without these solutions, UAV misuse will hurt the potential benefits that UAVs offer and drive the public against them