Applying Deep Learning to Estimate Fruit Yield in Agriculture 4.0 Systems

Over the last few years, with the advances in Information and Communications Technology (ICT) and the increasing human needs, industry has been reshaping itself. A new industrial revolution is emerging, and it is called Industry 4.0. This revolution intends to digitize the market and make it as intelligent as possible. As the history tells, every time there is an industry revolution, the agricultural sector benefits from it. Agriculture 4.0 is ongoing, and it is marked by intelligence and data. It aims to make the agricultural sector more efficient, that is: producing more outputs (such as food, fibers, fuel and other raw materials) while using less inputs (e.g. water, fertilizers, pesticides). Additionally, it envisions to promote food security, by reducing food loss and waste during the “Farm to Fork” journey. A major challenge in the agricultural sector is forecasting food storage and marketing activities prior to harvesting. Nowadays, most farmers manually count fruits before harvesting, in order to estimate the production yield of their fields, as a means to manage storage and marketing activities. Manually counting fruits in large fields is a laborious, costly and time-consuming effort, which is often also error prone. A consequence of this outdated methodology is that it leads to food wastage, which can affect food security. The developed work along this dissertation is an entry point to a system that is capable of estimating the production yield of a whole orchard, while being capable of respecting the required time constraints of each case study. With data taken with a smartphone, the developed system was able to accurately estimate the number of fruits present in tree sides, registering accuracies up to 98%. The high accuracy and speed results were possible due to the combination of state-of-the-art object detection and tracking techniques. To achieve this, a large model of Scaled YOLOv4 was combined with an online Multiple Object Tracking (MOT) framework based on Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT). Furthermore, this results validated the viability of implementing a proposed system, capable of estimating the fruit yield of a whole tree and, consequently, the production yield of the whole orchard, that is both low in complexity, easy-to-use, fast and reliable.O avanço das ICTs, juntamente com as necessidades humanas, está a proporcionar uma nova revolução industrial, designada de Indústria 4.0. Esta revolução visa uma digitalização do mercado, assim como torná-lo mais inteligente. Sempre que uma revolução industrial toma lugar, o setor agrícola beneficia disso, herdando as tecnologias que fazem parte de tal revolução. A agricultura 4.0 está em progresso, e é marcada por inteligência e informação. Esta revolução tem como objetivo tornar o setor agrícola mais eficiente, isto é: produzir mais (por exemplo comida, fibras, combustível e outras matérias-primas) com menos (e.g. água, fertilizantes, pesticidas). Adicionalmente, esta revolução visa a promoção da segurança alimentar, através da redução da perda e do desperdício de comida. Um grande problema no setor agrícola reside no planeamento de armazenamento e marketing de alimentos, antes da sua colheita. Na realidade, a maioria dos agricultores realiza o processo de contagem de frutos, do seu campo agrícola, manualmente, a fim de planear o espaço necessário para armazenar os mesmos e planear as suas vendas. A contagem manual de frutos é uma tarefa dispendiosa, que consome uma grande porção de tempo, tediosa, e propícia a erros. Uma consequência desta metodologia de trabalho é o desperdício alimentar, o qual leva ao comprometimento da segurança alimentar. O trabalho desenvolvido ao longo desta dissertação é um ponto de partida para um sistema que é capaz de estimar o rendimento de produção de um pomar inteiro, e ao mesmo tempo capaz de respeitar as restrições temporais de cada caso de estudo. Através de dados adquiridos com um smartphone, o sistema desenvolvido é capaz de estimar o número de frutos presentes em faces de árvores, registando eficácias tão altas como 98%. Os resultados obtidos foram possíveis devido às técnicas implementadas, que contaram com a combinação de metodologias de estado de arte de deteção e rastreamento de objetos. Um modelo da arquitetura Scaled YOLOv4 foi combinado com uma framework baseada em Deep SORT capaz de rastrear múltiplos objetos numa sequência de imagens. Os resultados obtidos validam a viabilidade da implementação de um sistema proposto, que ambiciona ser simples, fácil de usar, rápido e fiável na contagem de frutos de uma árvore inteira e, consequentemente, na estimação do rendimento de produção de um pomar inteiro

A Review of the Factors Affecting Adoption of Precision Agriculture Applications in Cotton Production

Precision agriculture (PA) is a modern farming management system adopted throughout the world, which employs cropping practices by observing and measuring the temporal and spatial variability in fields to enhance the sustainability of agricultural production through more efficient use of land, water, fuel, fertilizer, and pesticides. The efficiency of precision agriculture technologies (PAT) in agricultural production mainly depends on the use of site-specific agricultural inputs accurately through decision support mechanisms by observing and measuring the variables such as soil condition, plant health, and weed intensity. Although there have been significant developments in PAT, especially remote sensing as a key source of information available in support of PA in recent years, its adoption has been very slow by farmers due to a variety of reasons. The main aim of this chapter is to provide a critical overview of how recent developments in sensing technologies, geostatistical analysis, data fusion, and interpolation techniques can be used in the cotton production systems to optimize yields while minimizing water, chemical pesticide, and nitrogen inputs and analysis the main factors influencing the adoption of PAT by cotton farmers. Therefore, this chapter includes a compressive literature survey of the studies done on the current use and trends of PAT, and on farm level use of PA in cotton production worldwide

Adoption of artificial intelligence based technologies in sub-saharan african agriculture

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceSub-Saharan Africa (SSA) is currently facing numerous agriculture related challenges such as climate change, lacking infrastructure, and limited institutional as well as economic support. However, current research does not provide holistic solutions to this problem. This study aims to shed light on this topic through the development of a model that can be used to assess the solution potential as well as high-level implementation requirements of selected artificial intelligence (AI) based agriculture technologies in the context of SSA. To thoroughly develop the above-mentioned model a design science approach was followed. First an in depth (systematic) literature review was conducted where the agriculture related challenges in SSA and state-of-the-art AI-based agriculture technologies are detailed. This step was followed by the creation of a model that aims to find a nexus between the researched challenges and available technologies as potential solutions. Furthermore, the framework outlines context specific technology adoption requirements. Lastly, expert interviews were conducted to validate and revise the proposed model. The final framework clearly highlights the positive impact AI based technologies can have in SSA’s agriculture and the basic conditions that need to be met to successfully implement them

Patentes de Aplicativos Móveis Acerca do Uso de Agrotóxicos e Saúde do Trabalhador Rural: uma prospecção científica e tecnológica

The present work aims to identify the scientific and technological production related to mobile applications about the use of pesticides and the health of rural workers. The searches were carried out in the WIPO, EPO/Espacenet, INPI, Web of Science, Science Direct, Scopus, CAPES and SciELO databases, using the keywords: pesticide, pesticide, "mobile application", "rural worker", health , consultation and prevention. A total of 298 scientific articles were found, of which 12 were included in this study. Regarding patents, 185 records were located, of which 6 were mobile applications, however, none had a direct relationship with the specific topic. Thus, based on the analysis of the results, it was possible to observe that most of the productions related to the theme were scientific articles, evidencing a gap in this technological area.O presente trabalho tem por objetivo identificar a produção científica e tecnológica relacionada aos aplicativos móveis acerca do uso de agrotóxicos e saúde do trabalhador rural. Trata-se de uma prospecção científica e tecnológica realizada utilizando metodologia sistemática. Foram encontrados 298 artigos científicos, destes, 12 foram incluídos na síntese qualitativa para composição deste estudo. Em relação às patentes, foram localizados 185 registros, dos quais, 6 se tratavam de aplicativos móveis, porém, nenhum destes era relacionado ao tema específico. Com base na análise dos resultados, foi possível observar que a maioria das produções relacionadas ao tema tratavam-se de artigos científicos, havendo uma carência de depósitos de patentes. Portanto, os resultados obtidos através deste estudo demonstram a existência de uma lacuna de investimento nessa área tecnológica e a necessidade de maior incentivo financeiro em pesquisas nesse setor

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities