Classifying Barako coffee leaf diseases using deep convolutional models

This work presents the application of recent Deep Convolutional Models (DCM) to classify Barako leaf diseases. Several selected DCMs performed image classification tasks using Transfer Learning and Fine-Tuning, together with data preprocessing and augmentation. The collected dataset used totals to 4,667. Each labeled into four different classes, which included Coffee Leaf Rust (CLR), Cercospora Leaf Spots (CLS), Sooty Molds (SM), and Healthy Leaves (HL). The DCMs were trained using the partial 4,023 images and validated with the remaining 644. The classification results of the trained models VGG16, Xception, and ResNetV2-152 attained overall accuracies of 97%, 95%, and 91%, respectively. By comparing in terms of True Positive Rate (TPR), we found that Xception has the highest number of correct classifications of CLR, VGG16 with SM, and CLS, while ResNetV2-152 with the lowest TPR for CLR. The evaluated results indicate that the use of Deep Convolutional Models with an adequate amount of data, proper fine-tuning, preprocessing, and transfer learning can yield efficient classifiers for identifying several Barako leaf diseases. This work primarily contributes to the growing field of deep learning, specifically for helping farmers improve their diagnostic process by providing a solution that can automatically classify Barako leaf diseases

METODE K-NEAREST NEIGHBOR DAN FITUR WARNA UNTUK KLASIFIKASI DAUN SIRIH BERDASARKAN CITRA DIGITAL

Sirih (Piper betle L.) merupakan spesies dalam genus Piper yang sangat dikenal masyarakat. Sirih terdiri dari beberapa jenis yaitu sirih merah, sirih wulung, sirih hijau, sirih emas, dan sirih hitam. Namun, masyarakat masih memerlukan bantuan untuk membedakan berbagai jenis sirih. Penelitian ini menerapkan teknik image processing untuk mendeteksi daun sirih yang berbeda menggunakan teknik klasifikasi dengan tahapan segmentasi dan ekstraksi ciri. Metode klasifikasi yang digunakan adalah metode K-Nearest Neighbor (KNN) yang dilakukan pada objek daun sirih untuk menentukan jenis daun sirih. Metode ini terdiri dari 5 tahap yaitu deteksi Region of Interest (ROI), preprocessing, segmentasi, ekstraksi fitur orde 1, dan klasifikasi. Data citra yang digunakan sebanyak 360, terbagi menjadi 300 data latih dan 60 data uji. Hasil klasifikasi kemudian dibagi menjadi tiga jenis sirih yaitu sirih merah, sirih hijau, dan sirih hitam. Tingkat akurasi hasil klasifikasi jenis daun sirih dideteksi menggunakan Confusion Matrix Multi Class berdasarkan kedekatan karakteristik statistik. Hasil penelitian menunjukkan bahwa fitur Orde 1 yang digunakan dalam penelitian ini sesuai dengan sistem klasifikasi daun sirih. Hasil uji klasifikasi menggunakan Confusion Matrix Multi yang mencapai nilai akurasi sebesar 97,77%. Hasil penelitian menunjukkan bahwa fitur Orde 1 yang digunakan dalam penelitian ini sesuai dengan sistem klasifikasi daun sirih. Hasil uji klasifikasi menggunakan Confusion Matrix Multi yang mencapai nilai akurasi sebesar 97,77%. Hasil penelitian menunjukkan bahwa fitur Orde 1 yang digunakan dalam penelitian ini sesuai dengan sistem klasifikasi daun sirih. Hasil uji klasifikasi menggunakan Confusion Matrix Multi yang mencapai nilai akurasi sebesar 97,77%

Agriculture in Africa: the emerging role of artificial intelligence.

This chapter critically considers the application of artificial intelligence (AI) to agriculture in Africa. It contends that, while African countries can utilise AI to address agricultural challenges, realising the full potential of AI in agriculture requires the judicious adaptation of pervasive AI technologies to serve African interests. Africa's young, vibrant population along with the movement of people, goods and services around the continent, promoted under the African Union's (AU) Agenda 2063 provide a fecund platform for AI-driven agricultural transformation. This is pivotal because of the multilayered agricultural paradoxes on the continent. For instance, Africa is endowed with an abundance of uncultivated arable land and diverse agro-ecological zones, from rain-forest vegetation to dry and arid vegetation, which engender the growth of wide-ranging food and cash crops, yet it suffers an alarming increase in food insecurity. An AU, United Nations (UN) Economic Commission for Africa (UNECA) and Food and Agriculture Organisation of the UN (FAO) Report on Food Security and Nutrition in Africa confirmed that 281.6 million people on the continent, comprising one-fifth of the population, faced hunger in 2020; 346.4 million Africans suffered from severe food insecurity while 452 million suffered from moderate food insecurity in the same year

Concept demonstrator for a decision support tool for agricultural applications

Industrial Engineerin

Wheat Improvement

This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

Perceptual fail: Female power, mobile technologies and images of self

Like a biological species, images of self have descended and modified throughout their journey down the ages, interweaving and recharging their viability with the necessary interjections from culture, tools and technology. Part of this journey has seen images of self also become an intrinsic function within the narratives about female power; consider Helen of Troy “a face that launched a thousand ships” (Marlowe, 1604) or Kim Kardashian (KUWTK) who heralded in the mass mediated ‘selfie’ as a social practice. The interweaving process itself sees the image oscillate between naturalized ‘icon’ and idealized ‘symbol’ of what the person looked like and/or aspired to become. These public images can confirm or constitute beauty ideals as well as influence (via imitation) behaviour and mannerisms, and as such the viewers belief in the veracity of the representative image also becomes intrinsically political manipulating the associated narratives and fostering prejudice (Dobson 2015, Korsmeyer 2004, Pollock 2003). The selfie is arguably ‘a sui generis,’ whilst it is a mediated photographic image of self, it contains its own codes of communication and decorum that fostered the formation of numerous new digital communities and influenced new media aesthetics . For example the selfie is both of nature (it is still a time based piece of documentation) and known to be perceptually untrue (filtered, modified and full of artifice). The paper will seek to demonstrate how selfie culture is infused both by considerable levels of perceptual failings that are now central to contemporary celebrity culture and its’ notion of glamour which in turn is intrinsically linked (but not solely defined) by the province of feminine desire for reinvention, transformation or “self-sexualisation” (Hall, West and McIntyre, 2012). The subject, like the Kardashians or selfies, is divisive. In conclusion this paper will explore the paradox of the perceptual failings at play within selfie culture more broadly, like ‘Reality TV’ selfies are infamously fake yet seem to provide Debord’s (1967) illusory cultural opiate whilst fulfilling a cultural longing. Questions then emerge when considering the narrative impact of these trends on engendered power structures and the traditional status of illusion and narrative fiction