Technological Innovation for Measurements on Crop Physiological and Agronomic Traits

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 11705

Special Issue Editor

1. Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
2. School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
Interests: plant phenomics; digital agriculture; crop physiology; breeding, genomics; precision agriculture; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As global food demands continue to increase with a growing world population, yield improvement achieved using traditional methods has become insufficient to meet current and future food requirements. This has led to the development of novel techniques in agriculture including phenomics, remote sensing, smart sensors, and precision agriculture. These technological innovations are playing key roles for faster and automated measurements of crop physiological and agronomic traits for breeding of improved crop varieties.

These technologies comprise digital sensors, cameras, imaging systems, robotics, UAVs, satellite-based systems, data-processing algorithms, and computer learning. These advancements are increasing the precision, accuracy, and throughput of data collection, while reducing costs and resource usage, as well as improving our understanding of novel crop traits previously unexplored due to method or resource constraints.

In this Special Issue, we aim to cover advanced digital, automated, and reliable measurements and data analytics for traits such as growth (e.g., biomass, growth rate, yield), morphological (plant height, area, branching, architecture), phenological (growth stages), plant health (greenness, senescence), and physiological (canopy temperature, chlorophyll, nitrogen, photosynthesis). These measurements are deployed in crops under various scenarios, including but not limited to biotic and abiotic stress tolerance, increasing nutrient use efficiency, crop monitoring, and yield and quality improvement.

Dr. Surya Kant
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital sensors
  • precision agriculture
  • machine learning
  • crop stress tolerance (e.g., nutrients, diseases, drought, cold and heat)
  • crop growth, biomass, and yield
  • plant phenotyping
  • remote sensing
  • UAV, satellite

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 6718 KiB  
Article
Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach
by Munmi Gogoi, Vikash Kumar, Shahin Ara Begum, Neelesh Sharma and Surya Kant
Agriculture 2023, 13(8), 1505; https://doi.org/10.3390/agriculture13081505 - 27 Jul 2023
Cited by 2 | Viewed by 2163
Abstract
Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant [...] Read more.
Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant leaves, but training CNNs requires large datasets of labelled images, which can be expensive and time-consuming. Here, we have experimented a 3-Stage CNN architecture with a transfer learning approach that utilises a pre-trained CNN model fine-tuned on a small dataset of rice disease images. The proposed approach significantly reduces the required training data while achieving high accuracy. We also incorporated deep learning techniques such as progressive re-sizing and parametric rectified linear unit (PReLU) to enhance rice disease detection. Progressive re-sizing improves feature learning by gradually increasing image size during training, while PReLU reduces overfitting and enhances model performance. The proposed approach was evaluated on a dataset of 8883 and 1200 images of disease and healthy rice leaves, respectively, achieving an accuracy of 94% when subjected to the 10-fold cross-validation process, significantly higher than other methods. These simulation results for disease detection in rice prove the feasibility and efficiency and offer a cost-effective, accessible solution for the early detection of rice diseases, particularly useful in developing countries with limited resources that can significantly contribute toward sustainable food production. Full article
Show Figures

Figure 1

20 pages, 27365 KiB  
Article
An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms
by Bashar Igried, Shadi AlZu’bi, Darah Aqel, Ala Mughaid, Iyad Ghaith and Laith Abualigah
Agriculture 2023, 13(4), 889; https://doi.org/10.3390/agriculture13040889 - 18 Apr 2023
Cited by 1 | Viewed by 1420
Abstract
Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing [...] Read more.
Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction. Full article
Show Figures

Figure 1

18 pages, 3601 KiB  
Article
Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping
by Emily Thoday-Kennedy, Bikram Banerjee, Joe Panozzo, Pankaj Maharjan, David Hudson, German Spangenberg, Matthew Hayden and Surya Kant
Agriculture 2023, 13(3), 620; https://doi.org/10.3390/agriculture13030620 - 04 Mar 2023
Cited by 2 | Viewed by 1669
Abstract
Safflower (Carthamus tinctorius L.) is a highly adaptable but underutilized oilseed crop capable of growing in marginal environments, with crucial agronomical, commercial, and industrial uses. Considerable research is still needed to develop commercially relevant varieties, requiring effective, high-throughput digital phenotyping to identify [...] Read more.
Safflower (Carthamus tinctorius L.) is a highly adaptable but underutilized oilseed crop capable of growing in marginal environments, with crucial agronomical, commercial, and industrial uses. Considerable research is still needed to develop commercially relevant varieties, requiring effective, high-throughput digital phenotyping to identify key selection traits. In this study, field trials comprising a globally diverse collection of 350 safflower genotypes were conducted during 2017–2019. Crop traits assessed included phenology, grain yield, and oil quality, as well as unmanned aerial vehicle (UAV) multispectral data for estimating vegetation indices. Phenotypic traits and crop performance were highly dependent on environmental conditions, especially rainfall. High-performing genotypes had intermediate growth and phenology, with spineless genotypes performing similarly to spiked genotypes. Phenology parameters were significantly correlated to height, with significantly weak interaction with yield traits. The genotypes produced total oil content values ranging from 20.6–41.07%, oleic acid values ranging 7.57–74.5%, and linoleic acid values ranging from 17.0–83.1%. Multispectral data were used to model crop height, NDVI and EVI changes, and crop yield. NDVI data identified the start of flowering and dissected genotypes according to flowering class, growth pattern, and yield estimation. Overall, UAV-multispectral derived data are applicable to phenotyping key agronomical traits in large collections suitable for safflower breeding programs. Full article
Show Figures

Figure 1

11 pages, 1714 KiB  
Article
Evaluation of LAI Dynamics by Using Plant Canopy Analyzer and Its Relationship to Yield Variation of Soybean in Farmer Field
by Shuhei Yamamoto, Naoyuki Hashimoto and Koki Homma
Agriculture 2023, 13(3), 609; https://doi.org/10.3390/agriculture13030609 - 01 Mar 2023
Cited by 1 | Viewed by 1602
Abstract
Soybean yield largely varies spatially and yearly in farmer fields. Appropriate growth diagnosis is recommended to stabilize the yield. Leaf area index (LAI) is a representative diagnostic item, but an evaluation method of LAI dynamics with growth has not been established. In this [...] Read more.
Soybean yield largely varies spatially and yearly in farmer fields. Appropriate growth diagnosis is recommended to stabilize the yield. Leaf area index (LAI) is a representative diagnostic item, but an evaluation method of LAI dynamics with growth has not been established. In this study, we utilized a growth function consisting of an exponential function and a power math function. Parameters were derived from the growth function to be analyzed with yield variability. The LAI was measured weekly by a plant canopy analyzer in farmer fields for 4 years. The dynamics were parameterized by fitting the growth function. The relationship between the parameters of LAI dynamics and soybean yield was analyzed. The growth function was well fitted to measured LAI at R2 = 0.82~0.90 and RMSE = 0.54~0.69 m2 m−2. The parameters of the growth function, such as maximum LAI (LAImax) and cumulative temperature at maximum LAI (TLAImax), quantified the spatial and yearly differences in LAI dynamics, partly explaining those in the yield. The growth function utilized in this study is considered a robust method to quantify LAI dynamics and to diagnose soybean production. The quantification of LAI dynamics may help to develop crop growth monitoring with UAVs (Unmanned Aerial Vehicles) remote sensing as a new diagnostic tool. Full article
Show Figures

Figure 1

25 pages, 10850 KiB  
Article
Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images
by Muhammad Riaz Hasib Hossain and Muhammad Ashad Kabir
Agriculture 2023, 13(3), 574; https://doi.org/10.3390/agriculture13030574 - 27 Feb 2023
Cited by 4 | Viewed by 4004
Abstract
Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated [...] Read more.
Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation. Full article
Show Figures

Figure 1

Back to TopTop