Kenshi Sakai

Prediction of citrus yield from airborne hyperspectral imagery

Recent advances in spectral imaging technology have enabled the development of models that estimate various crop parameters from spectral imagery data. We developed partial least square (PLS) models to predict fruit yield of Satsuma mandarin using airborne hyperspectral imagery obtained several months before harvesting. Hyperspectral images in the 72 visible and near-infrared (NIR) wavelengths (from 407 to 898xa0nm) were acquired over a citrus orchard during the early growing seasons of 2003, 2004 and 2005. The canopy features of individual trees were identified using pixel-based average spectral reflectance values for all 72 wavelengths from the acquired images. The acquired canopy features were then used as prediction variables to develop yield prediction models. These were developed using three techniques: (1) normalized difference vegetation index (NDVI), simple ratio (SR) and photochemical reflectance index (PRI), (2) conventional multiple linear regression (MLR) models, and (3) PLS regression models. As we intended to predict yield several months before the harvesting season (generally late December), the conventional techniques (vegetation indices and MLR) did not predict well. In contrast, PLS models gave successful predictions for the three years. These results confirmed the hypothesized correlation between canopy features and citrus yield. The successful forecasting of yields several months or even one year ahead of the harvest season is expected to contribute to planning harvest schedules, generating prescription maps for dealing with fluctuations of yield in specific trees, control measures, and management practices.

Estimation of citrus yield from canopy spectral features determined by airborne hyperspectral imagery

Hyperspectral imagery has become increasingly available in recent years and this has necessitated the evaluation of its potential for crop monitoring and precision agriculture applications. The potential of using airborne hyperspectral imagery to develop yield prediction models for citrus fruits was examined in this paper. Hyperspectral images in 72 visible and near-infrared (NIR) wavelengths (407–898 nm) were acquired over a citrus orchard in Japan by an Airborne Imaging Spectrometer for Applications (AISA) Eagle system. The canopy spectral features of individual trees were identified using pixel-based average spectral reflectance values at various wavelengths from the acquired images, which were then used to develop yield prediction models. Yield prediction models were developed using three different techniques: (i) three commonly employed vegetation indices, i.e. the normalized difference vegetation index (NDVI), simple ratio (SR) and photochemical reflectance index (PRI); (ii) a few significant wavelengths; and (iii) partial least squares (PLS) regression factors. Greater prediction accuracy was obtained with PLS models than with the models based on NDVI, SR or PRI, or the significant wavelengths. PLS models showed a significant correlation between hyperspectral imagery data and actual citrus yield for data acquired in 2003 and 2004. These results confirmed the hypothesized correlation between canopy spectral features and citrus yield. This information is valuable for forecasting yields, planning harvest schedules and generating prescription maps for the application of tree-specific alternate bearing control measures and management practices.

Inter-Relationships Between Canopy Features and Fruit Yield in Citrus as Detected by Airborne Multispectral Imagery

The objective of this research was to examine the inter-relationships between canopy features and the fruit yield of citrus crops. Satsuma Mandarin (Citrus unshiu Marc.), a native citrus variety in southeastern Asia, grown in an orchard located at Nebukawa Agricultural Research Station, Kanagawa prefecture, Japan, was used for this preliminary analysis. Airborne multispectral images in the red, green, blue, and near-infrared (NIR) bands with a high spatial resolution of 0.2 ×0.2 m were acquired over the experimental site at four time periods in 2002 and 2003. Images based on normalized difference vegetation index (NDVI) were generated with ERDAS Imagine 8.6 software. From these images, thresholded pixel counts (TPCs), indicators of the relative leaf areas of several leaf types in each canopy, for 48 selected tree samples were extracted using a program developed in MATLAB R12. Pearsons correlation analysis was employed to examine the relationships between each of the TPCs and the fruit yields of citrus in 2002, 2003, and 2004. Results indicated that some TPCs showed a higher correlation with citrus fruit yield than those corresponding to the entire canopy size, particularly the TPCs extracted from the visible red, green, and blue wavelengths. The TPCs corresponding to the mature leaves before the fast vegetative growth (May) were found to be significantly correlated with the fruit yield of the same growing season, while those corresponding to the younger leaves during this period were more significantly correlated with the fruit yields of the previous and the following growing seasons. These results confirmed the inter-relationships between canopy features and the fruit yield of citrus crops. This information also implies an unmatched energy allocation dynamic between different leaf types within the canopy, which may lead to an unsynchronized leaf energy contribution, direct (mature leaves) or delayed (younger leaves), to the fruiting of citrus crops. In addition, the models based on the TPCs extracted from early seasons images demonstrated the potential of airborne multispectral imagery to forecast the fruit yield of citrus trees. The obtained yield estimates can provide valuable information for planning fruit harvest schedules and generating prescription maps for tree-specific management practices on an individual tree basis. However, further investigations are necessary before these models can be applied in a practical situation.

Nondestructive Analysis of Chlorpyrifos on Apple Skin Using UV Reflectance

Analysis of chlorpyrifos using UV was performed to assess the possibility of nondestructive detection of pesticides on agricultural products. Absorbance of chlorpyrifos dissolved in acetonitrile was positively proportional to its concentration at 229 and 290 nm at limits of detection (LOD) of 0.0233 and 0.0113 mg kg-1, and limits of quantification (LOQ) of 0.0778 and 0.0378 mg kg-1, respectively. This suggested that a UV method is suitable for detection and quantification of chlorpyrifos. UV reflectance changes were used to estimate the amount of chlorpyrifos dried on apple skin. Differences between reflectance at 280 and 300 nm were positively proportional to the amount of chlorpyrifos standard dried on apple skin, and the LOD was 0.0849 mg cm-2. Reflectance differences between 260 and 290 nm were positively proportional to the amount of chlorpyrifos included in water-dispersible Dursban dried on apple skin, and the LOD was 0.000214 mg cm-2. These LOD values were below the amount required for acute intoxication in mammals resulting from oral dosage. This UV optical method is practical for 100% nondestructive inspection and detection of chemicals present on product surfaces and thus may be suitable for use in sorting lines. However, it may not be effective for detecting chemicals in products internal tissues as can be achieved through immunochemical techniques and gas chromatography with mass spectrometry, which, while more sensitive than this UV method, are destructive and therefore not suitable for 100% inspection of shipping products.

Understanding the alternate bearing phenomenon: Resource budget model.

We consider here the resource budget model of plant energy resources, which characterizes the ecological alternate bearing phenomenon in fruit crops, in which high and low yields occur in alternate years. The resource budget model is a tent-type map, which we study in detail. An infinite number of chaotic bands are observed in this map, which are separated by periodic unstable fixed points. These m bands chaotic attractors become m/2 bands when the period-m unstable fixed points simultaneously collide with the chaotic bands. The distance between two sets of coexisting chaotic bands that are separated by a period-1 unstable fixed point is discussed. We explore the effects of varying a range of parameters of the model. The presented results explain the characteristic behavior of the alternate bearing estimated from the real field data. Effects of noise are also explored. The significance of these results to ecological perspectives of the alternate bearing phenomenon is highlighted.

Direct coupling: a possible strategy to control fruit production in alternate bearing

We investigated the theoretical possibility of applying phenomenon of synchronization of coupled nonlinear oscillators to control alternate bearing in citrus. The alternate bearing of fruit crops is a phenomenon in which a year of heavy yield is followed by an extremely light one. This phenomenon has been modeled previously by the resource budget model, which describes a typical nonlinear oscillator of the tent map type. We have demonstrated how direct coupling, which could be practically realized through grafting, contributes to the nonlinear dynamics of alternate bearing, especially phase synchronization. Our results show enhancement of out-of-phase synchronization in production, which depends on initial conditions obtained under the given system parameters. Based on these numerical experiments, we propose a new method to control alternate bearing, say in citrus, thereby enabling stable fruit production. The feasibility of validating the current results through field experimentation is also discussed.

Chaos emerging in soil failure patterns observed during tillage: Normalized deterministic nonlinear prediction (NDNP) and its application

Real-world processes are often combinations of deterministic and stochastic processes. Soil failure observed during farm tillage is one example of this phenomenon. In this paper, we investigated the nonlinear features of soil failure patterns in a farm tillage process. We demonstrate emerging determinism in soil failure patterns from stochastic processes under specific soil conditions. We normalized the deterministic nonlinear prediction considering autocorrelation and propose it as a robust way of extracting a nonlinear dynamical system from noise contaminated motion. Soil is a typical granular material. The results obtained here are expected to be applicable to granular materials in general. From a global scale to nano scale, the granular material is featured in seismology, geotechnology, soil mechanics, and particle technology. The results and discussions presented here are applicable in these wide research areas. The proposed method and our findings are useful with respect to the application of nonlinear dynamics to investigate complex motions generated from granular materials.