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Plants
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Precision Farming Application in Crop Protection
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Precision Farming Application in Crop Protection

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 4035

Special Issue Editor

Prof. Dr. João Paulo Arantes Rodrigues Da Cunha

E-Mail Website
Guest Editor
Institute of Agricultural Sciences, Universidade Federal de Uberlândia, Rod. BR 050, Km 78, Uberlândia 38410-337, Minas Gerais, Brazil
Interests: pesticide application technology; precision farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing global demand for food, either because of population growth or poor distribution, requires successive increases in production. Given this, farmers need to use technologies that result in increased productivity, with techniques involved in the application of plant protection products and fertilizers being among these technologies. The application technology has an extremely relevant purpose because it should ensure the deposition of the agricultural inputs on the desired target, whether it is the soil, leaf, or an insect, efficiently, and also prevent losses to the environment.

The journal Plants will be publishing a Special Issue on Precision Farming Application in Crop Protection. This Special Issue will focus on innovative research to optimize pesticide and fertilizer applications. We invite authors to submit manuscripts on the technical development and practical performance of pesticide application technology. We welcome new research covering all relevant topics, including pest and disease detection, efficient pesticide spray, spot spray, and innovative methods for applying pesticides.

Prof. Dr. João Paulo Arantes Rodrigues Da Cunha
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. Plants is an international peer-reviewed open access semimonthly 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 2700 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

  • pesticide application technology
  • precision farming
  • sprayers
  • crop protection
  • agricultural spraying
  • spray drones
  • chemical and biological control
  • precision agricultural aviation

Published Papers (3 papers)

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Research

15 pages, 9407 KiB  
Article
Simulated Drift of Dicamba and Glyphosate on Coffee Crop
by Renan Zampiroli, João Paulo Arantes Rodrigues da Cunha and Cleyton Batista de Alvarenga
Plants 2023, 12(20), 3525; https://doi.org/10.3390/plants12203525 - 10 Oct 2023
Viewed by 888
Abstract
Weed management in areas adjacent to coffee plantations makes herbicide drift a constant concern, especially with the use of nonselective products such as dicamba. The objective of this study was to evaluate the phytotoxic effects of the herbicide dicamba alone and mixed with [...] Read more.
Weed management in areas adjacent to coffee plantations makes herbicide drift a constant concern, especially with the use of nonselective products such as dicamba. The objective of this study was to evaluate the phytotoxic effects of the herbicide dicamba alone and mixed with glyphosate as a result of simulated drift in a coffee-producing area. The study was conducted in duplicate at two different coffee cherry development stages. The study was performed with a randomized block design and a 2 × 5 + 1 factorial scheme with four replications using two herbicide spray solutions (dicamba and dicamba + glyphosate) and five low doses (0.25; 1; 5; 10; and 20%). Additionally, a control treatment without herbicide application was also employed. In this study, we evaluated the phytotoxic damage and biometric and productive parameters. Visual damages were observed with the use of dicamba and dicamba + glyphosate doses reduced by 0.25% to 5% in the first days after application. The main symptoms were new leaf epinasty, changes in the internodal distance, and plagiotropic branch curvature. Low doses led to reduced plant height and branch length. The treatments did not reduce productivity and performance but altered the physical classifications of grains. Full article
(This article belongs to the Special Issue Precision Farming Application in Crop Protection)
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19 pages, 5054 KiB  
Article
Real-Time Detection Algorithm for Kiwifruit Canker Based on a Lightweight and Efficient Generative Adversarial Network
by Ying Xiang, Jia Yao, Yiyu Yang, Kaikai Yao, Cuiping Wu, Xiaobin Yue, Zhenghao Li, Miaomiao Ma, Jie Zhang and Guoshu Gong
Plants 2023, 12(17), 3053; https://doi.org/10.3390/plants12173053 - 25 Aug 2023
Viewed by 1221
Abstract
Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. [...] Read more.
Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision ([email protected]) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms. Full article
(This article belongs to the Special Issue Precision Farming Application in Crop Protection)
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12 pages, 1949 KiB  
Article
Leaf Sample Size for Pesticide Application Technology Trials in Coffee Crops
by Roxanna Patricia Palma, João Paulo Arantes Rodrigues da Cunha and Denise Garcia de Santana
Plants 2023, 12(5), 1093; https://doi.org/10.3390/plants12051093 - 1 Mar 2023
Cited by 2 | Viewed by 1357
Abstract
Plot size, sample sufficiency, and number of repetitions are factors that affect the experimental errors or residuals and the expression of true differences among treatments. The objective of this study was to determine, using statistical models, the appropriate sample size for application technology [...] Read more.
Plot size, sample sufficiency, and number of repetitions are factors that affect the experimental errors or residuals and the expression of true differences among treatments. The objective of this study was to determine, using statistical models, the appropriate sample size for application technology experiments in coffee crops through the evaluation of foliar spray deposition and soil runoff in the ground-based application of pesticides. In the first stage, we determined the quantity of leaves per set and the volume of the solution for washing the leaves and extracting the tracer. We analyzed the variability between the coefficients of variation (CVs) of the amount of tracer extracted in two droplet classes (fine and coarse), for the different parts of the plants, and for the different quantities of leaves per set that were organized into intervals of five leaves (1–5, 6–10, 11–15, and 16–20). Less variability was found in the intervals with 10 leaves per set and using 100 mL of extraction solution. In the second stage, a field experiment was conducted using an entirely randomized design with 20 plots: 10 sprayed with fine droplets and 10 with coarse droplets. In each plot, 10 sets (samples) with 10 leaves each were collected from the upper and lower canopy of the coffee trees. Moreover, 10 Petri dishes were placed per plot and collected after application. Based on the results of the spray deposition (mass of tracer extracted per cm2 of leaf), we determined the optimal sample size using the maximum curvature and maximum curvature of the coefficient of variation methods. Higher variabilities were related to the targets that are more difficult to reach. Thus, this study determined an optimal sample size between five and eight sets of leaves for spray deposition, and four to five Petri dishes for soil runoff. Full article
(This article belongs to the Special Issue Precision Farming Application in Crop Protection)
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